{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zX4Kg8DUTKWO"
   },
   "outputs": [],
   "source": [
    "# 模拟生成时间序列\n",
    "# 模拟生成数据集\n",
    "# 搭建两个RNN神经网络，一个使用LR_scheduler机制调整学习率，另一个不做处理\n",
    "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "# https://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/lmoroney/dlaicourse/blob/master/TensorFlow%20In%20Practice/Course%204%20-%20S%2BP/S%2BP%20Week%203%20Lesson%202%20-%20RNN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cellView": "both",
    "colab": {},
    "colab_type": "code",
    "id": "D1J15Vh_1Jih"
   },
   "outputs": [],
   "source": [
    "try:\n",
    "  # %tensorflow_version only exists in Colab.\n",
    "  %tensorflow_version 2.x\n",
    "except Exception:\n",
    "  pass\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BOjujz601HcS"
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Zswl7jRtGzkk"
   },
   "outputs": [],
   "source": [
    "# 模拟生成时间序列\n",
    "def plot_series(time, series, format=\"-\", start=0, end=None):\n",
    "    plt.plot(time[start:end], series[start:end], format)\n",
    "    plt.xlabel(\"Time\")\n",
    "    plt.ylabel(\"Value\")\n",
    "    plt.grid(True)\n",
    "\n",
    "def trend(time, slope=0):\n",
    "    return slope * time\n",
    "\n",
    "def seasonal_pattern(season_time):\n",
    "    \"\"\"Just an arbitrary pattern, you can change it if you wish\"\"\"\n",
    "    return np.where(season_time < 0.4,\n",
    "                    np.cos(season_time * 2 * np.pi),\n",
    "                    1 / np.exp(3 * season_time))\n",
    "\n",
    "def seasonality(time, period, amplitude=1, phase=0):\n",
    "    \"\"\"Repeats the same pattern at each period\"\"\"\n",
    "    season_time = ((time + phase) % period) / period\n",
    "    return amplitude * seasonal_pattern(season_time)\n",
    "\n",
    "def noise(time, noise_level=1, seed=None):\n",
    "    rnd = np.random.RandomState(seed)\n",
    "    return rnd.randn(len(time)) * noise_level\n",
    "\n",
    "time = np.arange(4 * 365 + 1, dtype=\"float32\")  #1461\n",
    "baseline = 10\n",
    "series = trend(time, 0.1)  \n",
    "baseline = 10\n",
    "amplitude = 40\n",
    "slope = 0.05\n",
    "noise_level = 5\n",
    "\n",
    "# Create the series\n",
    "series = baseline + trend(time, slope) + seasonality(time, period=365, amplitude=amplitude)\n",
    "# Update with noise\n",
    "series += noise(time, noise_level, seed=42)\n",
    "\n",
    "split_time = 1000\n",
    "time_train = time[:split_time]\n",
    "x_train = series[:split_time]\n",
    "time_valid = time[split_time:]\n",
    "x_valid = series[split_time:]\n",
    "\n",
    "window_size = 20\n",
    "batch_size = 32\n",
    "shuffle_buffer_size = 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4sTTIOCbyShY"
   },
   "outputs": [],
   "source": [
    "# 模拟生成数据集\n",
    "#参数说明： 序列数据（一维数组），窗口大小，批次大小，随机缓存大小\n",
    "#输出： (特征， 标签)\n",
    "def windowed_dataset(series, window_size, batch_size, shuffle_buffer):\n",
    "  dataset = tf.data.Dataset.from_tensor_slices(series)\n",
    "  dataset = dataset.window(window_size + 1, shift=1, drop_remainder=True)\n",
    "  dataset = dataset.flat_map(lambda window: window.batch(window_size + 1))\n",
    "  dataset = dataset.shuffle(shuffle_buffer).map(lambda window: (window[:-1], window[-1]))\n",
    "  dataset = dataset.batch(batch_size).prefetch(1)\n",
    "  for x,y in dataset:\n",
    "    print(\"x = \", x.numpy())\n",
    "    print(\"y = \", y.numpy())\n",
    "  return dataset\n",
    "\n",
    "# pytorch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "L4nblWkqg1NL"
   },
   "outputs": [],
   "source": [
    "# 搭建SimpleRNN神经网络，使用LR_scheduler机制调整学习率\n",
    "tf.keras.backend.clear_session()\n",
    "tf.random.set_seed(51)\n",
    "np.random.seed(51)\n",
    "\n",
    "train_set = windowed_dataset(x_train, window_size, batch_size=128, shuffle_buffer=shuffle_buffer_size)\n",
    "#神经网络的搭建\n",
    "model = tf.keras.models.Sequential([\n",
    "    #匿名层：处理输入参数，将输入\n",
    "  tf.keras.layers.Lambda(lambda x: tf.expand_dims(x, axis=-1),  # tf.expand_dims(x,axis=-1) x是input,-1代表最后一维，对应y=时间序列\n",
    "                      input_shape=[None]),\n",
    "  tf.keras.layers.SimpleRNN(40, return_sequences=True),#RNN单元数量为40，返回输出序列中的完整序列\n",
    "  tf.keras.layers.SimpleRNN(40),\n",
    "  tf.keras.layers.Dense(1), #神经元个数为1\n",
    "  tf.keras.layers.Lambda(lambda x: x * 100.0)#x的规则\n",
    "])\n",
    "#运用lrscheduler来进行学习率的调节 epoch=10的-8次 * 10^(epoch/20)\n",
    "lr_schedule = tf.keras.callbacks.LearningRateScheduler(\n",
    "    lambda epoch: 1e-8 * 10**(epoch / 20))\n",
    "#SGD随机梯度下降，lr=学习率，momentum：动量参数\n",
    "optimizer = tf.keras.optimizers.SGD(lr=1e-8, momentum=0.9)\n",
    "#model.compile()方法用于在配置训练方法时候，告知训练时所用的优化器、损失函数和准确率评测\n",
    "model.compile(loss=tf.keras.losses.Huber(),#损失函数\n",
    "              optimizer=optimizer,#优化器\n",
    "              metrics=[\"mae\"])#准确率评判\n",
    "history = model.fit(train_set, epochs=100, callbacks=[lr_schedule])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5He3pp-Hj758"
   },
   "outputs": [],
   "source": [
    "plt.semilogx(history.history[\"lr\"], history.history[\"loss\"])\n",
    "plt.axis([1e-8, 1e-4, 0, 30])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6y1KMowRkHkC"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 81.7535 - mae: 82.2534\n",
      "Epoch 2/100\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 21.9357 - mae: 22.4301\n",
      "Epoch 3/100\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 15.9201 - mae: 16.4144\n",
      "Epoch 4/100\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 12.2878 - mae: 12.7813\n",
      "Epoch 5/100\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 10.1496 - mae: 10.6398\n",
      "Epoch 6/100\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 16.3675 - mae: 16.8627\n",
      "Epoch 7/100\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 13.2591 - mae: 13.7505\n",
      "Epoch 8/100\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 9.0446 - mae: 9.5349\n",
      "Epoch 9/100\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 7.8271 - mae: 8.3149\n",
      "Epoch 10/100\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 9.7639 - mae: 10.2559\n",
      "Epoch 11/100\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 15.4936 - mae: 15.9915\n",
      "Epoch 12/100\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 12.8534 - mae: 13.3449\n",
      "Epoch 13/100\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 11.7932 - mae: 12.2840\n",
      "Epoch 14/100\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 10.6166 - mae: 11.1111\n",
      "Epoch 15/100\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 10.1224 - mae: 10.6151\n",
      "Epoch 16/100\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 8.8419 - mae: 9.3300\n",
      "Epoch 17/100\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 9.3513 - mae: 9.8428\n",
      "Epoch 18/100\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 12.0222 - mae: 12.5149\n",
      "Epoch 19/100\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 10.2901 - mae: 10.7794\n",
      "Epoch 20/100\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 6.5791 - mae: 7.0645\n",
      "Epoch 21/100\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 5.5807 - mae: 6.0629\n",
      "Epoch 22/100\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 7.4439 - mae: 7.9309\n",
      "Epoch 23/100\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 5.9591 - mae: 6.4417\n",
      "Epoch 24/100\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 5.8886 - mae: 6.3708\n",
      "Epoch 25/100\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 5.4046 - mae: 5.8894\n",
      "Epoch 26/100\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 5.3916 - mae: 5.8747\n",
      "Epoch 27/100\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 6.6912 - mae: 7.1709\n",
      "Epoch 28/100\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 7.6007 - mae: 8.0895\n",
      "Epoch 29/100\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 6.4432 - mae: 6.9275\n",
      "Epoch 30/100\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 6.1663 - mae: 6.6474\n",
      "Epoch 31/100\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 5.9725 - mae: 6.4578\n",
      "Epoch 32/100\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 5.6571 - mae: 6.1431\n",
      "Epoch 33/100\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 5.3076 - mae: 5.7874\n",
      "Epoch 34/100\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 5.5510 - mae: 6.0302\n",
      "Epoch 35/100\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 5.5656 - mae: 6.0483\n",
      "Epoch 36/100\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 5.6769 - mae: 6.1603\n",
      "Epoch 37/100\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 6.0583 - mae: 6.5385\n",
      "Epoch 38/100\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 6.1616 - mae: 6.6422\n",
      "Epoch 39/100\n",
      "8/8 [==============================] - 0s 5ms/step - loss: 5.1359 - mae: 5.6148\n",
      "Epoch 40/100\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 6.0949 - mae: 6.5768\n",
      "Epoch 41/100\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 5.3352 - mae: 5.8160\n",
      "Epoch 42/100\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 5.6683 - mae: 6.1487\n",
      "Epoch 43/100\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 5.8894 - mae: 6.3718\n",
      "Epoch 44/100\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 6.1080 - mae: 6.5911\n",
      "Epoch 45/100\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 10.5188 - mae: 11.0116\n",
      "Epoch 46/100\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 6.9824 - mae: 7.4666\n",
      "Epoch 47/100\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 9.7012 - mae: 10.1909\n",
      "Epoch 48/100\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 6.1739 - mae: 6.6600\n",
      "Epoch 49/100\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 5.9774 - mae: 6.4611\n",
      "Epoch 50/100\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 6.2356 - mae: 6.7202\n",
      "Epoch 51/100\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 5.4952 - mae: 5.9777\n",
      "Epoch 52/100\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 5.8177 - mae: 6.3018\n",
      "Epoch 53/100\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 4.8927 - mae: 5.3720\n",
      "Epoch 54/100\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 4.7656 - mae: 5.2469\n",
      "Epoch 55/100\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 4.8019 - mae: 5.2788\n",
      "Epoch 56/100\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 4.4376 - mae: 4.9162\n",
      "Epoch 57/100\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 4.8943 - mae: 5.3779\n",
      "Epoch 58/100\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 6.0013 - mae: 6.4854\n",
      "Epoch 59/100\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 7.4086 - mae: 7.8929\n",
      "Epoch 60/100\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 5.9479 - mae: 6.4344\n",
      "Epoch 61/100\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 5.2884 - mae: 5.7716\n",
      "Epoch 62/100\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 6.5404 - mae: 7.0258\n",
      "Epoch 63/100\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 5.6378 - mae: 6.1215\n",
      "Epoch 64/100\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 4.8178 - mae: 5.2982\n",
      "Epoch 65/100\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 9.0214 - mae: 9.5108\n",
      "Epoch 66/100\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 10.3440 - mae: 10.8332\n",
      "Epoch 67/100\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 9.6055 - mae: 10.0963\n",
      "Epoch 68/100\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 14.8239 - mae: 15.3205\n",
      "Epoch 69/100\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 10.2673 - mae: 10.7575\n",
      "Epoch 70/100\n",
      "8/8 [==============================] - 0s 9ms/step - loss: 9.1353 - mae: 9.6240\n",
      "Epoch 71/100\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 9.7076 - mae: 10.1993\n",
      "Epoch 72/100\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 7.5435 - mae: 8.0325\n",
      "Epoch 73/100\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 5.5397 - mae: 6.0219\n",
      "Epoch 74/100\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 6.1952 - mae: 6.6783\n",
      "Epoch 75/100\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 5.4733 - mae: 5.9538\n",
      "Epoch 76/100\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 5.0732 - mae: 5.5484\n",
      "Epoch 77/100\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 6.5645 - mae: 7.0519\n",
      "Epoch 78/100\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 5.5320 - mae: 6.0086\n",
      "Epoch 79/100\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 4.9332 - mae: 5.4123\n",
      "Epoch 80/100\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 5.2718 - mae: 5.7490\n",
      "Epoch 81/100\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 5.0173 - mae: 5.4974\n",
      "Epoch 82/100\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 6.4041 - mae: 6.8908\n",
      "Epoch 83/100\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 6.1931 - mae: 6.6775\n",
      "Epoch 84/100\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 5.9964 - mae: 6.4772\n",
      "Epoch 85/100\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 8.5246 - mae: 9.0132\n",
      "Epoch 86/100\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 9.3089 - mae: 9.7991\n",
      "Epoch 87/100\n",
      "8/8 [==============================] - 0s 10ms/step - loss: 8.0444 - mae: 8.5317\n",
      "Epoch 88/100\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 9.0686 - mae: 9.5596\n",
      "Epoch 89/100\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 5.8893 - mae: 6.3686\n",
      "Epoch 90/100\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 6.2000 - mae: 6.6853\n",
      "Epoch 91/100\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 4.9398 - mae: 5.4237\n",
      "Epoch 92/100\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 5.0833 - mae: 5.5656\n",
      "Epoch 93/100\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 6.9800 - mae: 7.4664\n",
      "Epoch 94/100\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 6.7585 - mae: 7.2417\n",
      "Epoch 95/100\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 6.5650 - mae: 7.0508\n",
      "Epoch 96/100\n",
      "8/8 [==============================] - 0s 8ms/step - loss: 5.0535 - mae: 5.5320\n",
      "Epoch 97/100\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 5.9015 - mae: 6.3836\n",
      "Epoch 98/100\n",
      "8/8 [==============================] - 0s 7ms/step - loss: 7.6480 - mae: 8.1354\n",
      "Epoch 99/100\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 8.0130 - mae: 8.5005\n",
      "Epoch 100/100\n",
      "8/8 [==============================] - 0s 6ms/step - loss: 8.9552 - mae: 9.4459\n"
     ]
    }
   ],
   "source": [
    "# 搭建SimpleRNN神经网络，不调整学习率\n",
    "tf.keras.backend.clear_session()\n",
    "tf.random.set_seed(51)\n",
    "np.random.seed(51)\n",
    "\n",
    "dataset = windowed_dataset(x_train, window_size, batch_size=128, shuffle_buffer=shuffle_buffer_size)\n",
    "\n",
    "model = tf.keras.models.Sequential([\n",
    "  tf.keras.layers.Lambda(lambda x: tf.expand_dims(x, axis=-1),\n",
    "                      input_shape=[None]),\n",
    "  tf.keras.layers.SimpleRNN(40, return_sequences=True),\n",
    "  tf.keras.layers.SimpleRNN(40),\n",
    "  tf.keras.layers.Dense(1),\n",
    "  tf.keras.layers.Lambda(lambda x: x * 100.0)\n",
    "])\n",
    "#在需要将隐层的结果作为下一层的输入时，选择return_sequences=True\n",
    "\n",
    "optimizer = tf.keras.optimizers.SGD(lr=5e-5, momentum=0.9)\n",
    "model.compile(loss=tf.keras.losses.Huber(),\n",
    "              optimizer=optimizer,\n",
    "              metrics=[\"mae\"])\n",
    "history = model.fit(dataset,epochs=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ejBynEKekaKw"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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B4QZ//DZ3yNKGJvK5Zkvqr0c0ZDlV9uZozkSav85UbLSkdLQkdRSrduj+aiTM+cTJMQxPV0AQBEEsH06ThCxJkBGrGpd5DlhC9wWZXptDxgUZF1+d2QQuTJUBzM0hiyMZCVlWLE9c8RDlL3/+Wezd1o5C1UYuqSOb1DA6Uw2FNKPjlsqmI6o2CYIgiOXBbZKQJQkyYlXjMgZNUZDUNczAhqHWVlkGDpnnarVnDCHIMgkNCyEasuQUKjamSia+eXAQ3zw4iFxSx7WbW5FN6ihU7VAbjGhlZsl0UCFBRhAEsay4TRKyXLIcMkVR/kVRlIuKorwgLetUFOXbiqK85P/f4S9XFEX5G0VRTiiK8ryiKDct1XoRawuvf0wQjhQhS8n5ivYh65Q67u/0u/PPl2hSP2emYuPIYF68376dHfij778OuaSOomljpmKLjv9Rh6xiOSibJMgIgiCWk/VQZfkZAG+KLPtNAA8yxvYAeND/GwDeDGCP/++9AP5hCdeLWEMwP2TJQ4h6zOikaMiyXRoSfnnPQgVZHYesauOFwWkAwJMffgM+81OvxBW9LcgldRQqNmaqFtoyBhSl1iErW17IkkV7eRAEQRBLBo+0rDRLJsgYY48AmIgsvgvAZ/3bnwXw/dLyzzGP7wJoVxRl01KtG7F2cFxv5AUPRyYaJvV7j+nIGOK+DX6fsfkSzSHj5CsWXriQx+a2VMiJ68gkYLsMg1MVtKQMJDQ1VpC5rH6TWYIgCGLxcdnKDxYHlr/tRS9jbMi/PQyg17+9BcB56XED/jKCaIgbCVnGOWTRthcdvkOmq8qCf4R1HbKK55Bdu6UttLwr573nuYkSWpK6J8js2hwyAKiY8YLs6FAe+UifM4IgCOLScBmD2gRNwFYsqZ8xxhRFmXdsRlGU98ILa6K3txf9/f2LvWo1FAqFZXkfYv6cP1+F69goF7y8rcnxMfT39+PFwSB5/qnvfRcn0yrOn/HEzNjgWQBARxIL/l5tqSGtoQJ+kSVODY7i9JSL61vN0GsPjHrrM1E0UZmZBJiDM+cG0N8/ivGyi5yhCIH20COPoiNVe3T4hQeKeNNOA3ftTtTct5ag39vqhL631ct6/+6GhiswK+6Kb4PlFmQjiqJsYowN+SHJi/7yCwC2SY/b6i+rgTF2N4C7AWDfvn2sr69vCVfXo7+/H8vxPsT8eXDqBSTHh9DT3Yrjk2PYumkj+vr2onRoCHj+GQDAa1/jzaocefoc/u+xQ7jhmqvwH0efx+WbOtDX9+oFvS9jDOq374XLgFzKwGTJE3sXKyoYXNx649Xo2xfs0l0D0/irA48BAHZt3YSB8hi6e7rx98eKePrMJH7i5u0AzgEA9u57FXZ1Z2ver/yte9G1cSv6+q5Z0DqvFuj3tjqh7231st6/uy8PPoOLVn7Ft8Fym3TfAPBu//a7AXxdWv6TfrXlzQCmpdAmQdTF6x8T5IfxkKUWGi7u3X7L9Zvw9z92E67c2AIAuPPajQt+X0UJ8tbkmZR5v61Fb2RYeWcucLVaUgYSuoqiaeNpv7P/iYsFcX9cpaXlMP9/yi8jCIJYTNZ8p35FUT4PoA9At6IoAwA+AuBPAXxRUZT3ADgL4Ef9h98L4C0ATgAoAfippVovYm3BG/rxJHve9iJUZennibWkDLz1ZV6tyAO/9npcvmFhFZaclKGibDnIJmvzyaKCrEtK8M+ldBiaEhqjdHGmKm7HNYflQowS/gmCIBYXfmG/0iyZIGOMvbPOXW+IeSwD8ItLtS7E2sV1/bYX0T5ksiDTan9pu3tyl/zeXmK/FXLIOL2tyZrHZhIaSqbjJfXrWihBfzQfCLK45rBCkNmzp10+c24SV21siV0vgiAIIow3OmnlFVkT1BUQxMJxWbjthSGqLGsbwy42vNIy2u0/oatoSxs1j+dtMFpSOhKagnw5EGTyjMtGIcvZHLKSaeNHP/UkvrR/YI6fgiAIYn3DR/CtNCTIiFVNtFO/3qAP2WLD3zPqRPW2JmPbafCwZS6lI6GrmPHzzVqS4eeXGjhk0e7+USqWC9tlmCyZc/wUBEEQax/bcWtaDXGape1FE6wCQSycaKd+HrI0tNo+ZIsNd8h4Dhl3wHpbUrGPDxwyA4amipDlhpZweLMS45DZc0zq5/eXaAQTQRCE4OP3H8c7/+m7sfet+U79BLEciE79vhBLxFRZ6kvkkPF5ljxk2eMLq2hCP6cz692fS3oOGQ9DRgVZXFK/Ocekfn4FWDLtho8jCIJYTwxMljEwWYq9b7126ieIRUV06jd42wteZRns2ktVzhxte8GFWE9r/Dgm3q2/NaULJw+YmyCzXZ7UXyvIKpaD+w4PA5Acsio5ZARBEBzTdlGtF7JcB8PFCWLJYcxL2q9XZblU+WNA4JBlfYdsoy/I6jtk4RwyTo0gi0vqt+sn9f/Zt47h5/7tAJ4+MyEmCBTJISMIghCYtouqVT+HbCnPFXOF6uKJVQ0vVw4EmV9lyUOXS2hDiypLPyl/14YsEpqKPXVaatx57UaMzlTR25ISQ9ABoCeScxbb9sJ3yOJyyMYKXgL/hcky0v46yTlkZdOB6bixlZ8EQRDrAc8hc8AYqwlPOm7tspWAHDJiVRMMF+dtL8KNYZfUIdM1KAqQ8sXg9s4Mnvzw7bj9qp7Yx+/qzuJ3v+8aqKoSEmSyQ9aS1OMbw9q8yrK2D1lb2hOE+YoVm9T/Z986hp/8l6fm+/EIgiDWDKbjwmXhOcQcHmlZaUiQEasa0YfMCAsxnkO21CFLQ1Nh6EFyf1cuvuVFFDlkyYsBEpqKXEqPDVnyg0hcyLI15Tlf0yVLFAoUpb5mY4UqxqRJAARBEOsNnn8bl0fWLG0vKGRJrGp4Qz/uOHGhw7vzL2VawKsu68JY0RSuXDY595+TnNTPiwBShoq0oTWusow5mORS3vtOly3Y/uPk13BcBifmqpAgCGK9wKMHVctBLnKsdhh16ieIS8a7spEdsohTpi3dLs6HlfO8NZ6/NRdkh6zLb4eRTmhIJ7TYHDK7Qad+5mutfMUS9xerYUEWZ9MTBEGsFxo7ZNSpnyAuGccvV46OTuKhyuX4kV2/pQ03bW/Hjq7MnJ/D+6WlDBWtfg5YJqEjbWgoVp2asKXo1B8jyPiBZrochCzLUpWlJ8hoKDkxf6q2g888fpocVmLVU20kyKjtBUFcOrxTf9pvPSH6kYmk/qVfh909Lfjq+16DltTcqxi5Q5YyNCR1DQldRcrwHLInT43jNX/2UCg82Wh0Er9vomgGSf2WA9c/iTqMwXGCE2rJtPGRr78QGm5OEHE8cXIcv//NIzg4MLXSq0IQlwSPHlTt2ghEs7S9IEFGrGoc1xt5ccPWdnz0+6/Dqy/rAiDNtGwCGzoOnkPGw5ytKR1pQxVO30TRxLmJonh8o+HiPBw5XggEGWNAxT/wREOWz52bwmefPIunT08s9scimoz+4xfx3PmFiyk+xqte/yZi/XJkMI/Dg9MrvRpzRoQsY/Zl6tRPEIsAb3uhqQp+4uYdwnkSDpm28j+yOGSHDPDmW6YTGs6OByLspZGCuB2ELBkYC4eP+IFmrFAVwg0IWl9EQ5ZFf3mhSs1j1zofu+coPvnwiQU/n4d3ZhvZRaw/PnrPEfzRfx+Z9/OOD8/glz//rChAWi4a5pBRyJIgLh1WJxlTdOpvgqueOLhDxhvaXtnbgss35EJhxJcuBoJMPnhFT45crOUrdmiGJR+fZEccMt4SgwTZ2qdiO6jUGRczF0y7fqicWN8UqjbKC3BOv3d6HN88OCgaWi8XqyFkSW0viFWNwxgSMQ1kuBBbqjmWlwp3yHju26fe9XIAwDtesR0vXZzBx+87jhOSIDMl58tyGOSqbVu6b3i6Im7z8Umuy8CYfxWoKmJ5oUKCbK1j2m5s1e5c4Scvcsiah/d/4VncflUP7tq7ZUXXo2I5oZnBc4Ufr+IKlJYKufVPXMjSienevxKQQ0asanjIMoqqKlAQhC6bDd43LaWHW2Vcs7kVd+3dgj09OXzj4CBu+IP7MThVDjtkdrxDBgDD+UCQiZClH+IUcy7JIVs3VBsMVJ7r84HlPXkSjXngyAiePjO3/E/bcUVxz2JTtpwFVW9zYbRcrXguTJVxcSY4Lsb9HpqlUz85ZMSqxnXrW82a0hy9ZeIIcsjir4k2tnnzLafLFk6PFUMnxOjJUXYv5DAAD18GB0AXCaiiR9kMOWRrHm+g8qU4ZPXzboiVwXJYyBVvxA988gm88epevP+NexZ9PSqWuzCHjB+Plknk//y/HcDm9mBecL2QZTNcu5NDRqxqGjX009SlHZ10KYgqy0R8M9lbd28Qt4tVO5Ss38ghm5Fy0Ljwil6RcoesSA7ZmoccsrUFYwym4845hHx+soSzUrX2YlIxndjJIbPhuMtbKDJWqGJourFD5vWzXPlzBQkyYlXjNKiOUZXmFWTCIdPjBdlbrt+Ib/7SrQC8XDCrQVK/7TARmpXzwspW2CHjvcioynJ9YDsuHJddokPm55CRQ9YU8Isqa44OmXWJgrwRFdtZkFAPHLK5hyzlYqX5UqzamCwFkYO43wNjzZFvTIKMWNW4DWaQaU0syPhEgWSdcUuKoqC3zRupVKjYoXyLuJBliz/PUg5DRh0yy+VjlSiHbD3AhfulVFnyBGhyyJqDRg2i4x9/aYK8HrbjwnLYgvYLOYViLhwenMb1v38/To8tzOkrmQ6mSkHkoL5DtqCXX1RIkBGrmnptLwBveTMkasbB2100mn/ZkvQ6/xeq4dBAXMiSTwmYqViiyKFshgUZ/59fbVIOWfPwvVPjOLPAE049gkaYCz8hNxpqTywPQ9Nl9H38YZyfKAVtSOYghHh4s7IETX25yJ+Py8WZr8s3MFmG4zKcmyjN+71M24XtstCxLn6WJYUsCeKScRr0j9GU5rCh40honhCrl9TP71MVoFC1QleT0YOx7TDk/D4YRdNBLsFv+yFLFg4RFMghazo+8KWD+LtLaOAaBz/xLIZDZi7gxEssDqdGizgzXsLx4ZlAIM9BkHHhE5fEfqnwi72F5IE58wxZ8rYtC2nTExfqjE/qb45zBQkyYlVTr+0F4OWQNWvbC0P31quRQ6YoCnJJHcWqA8uWk/rDBzJLClkCXhg0bWii7QU/8AUO2cIPcMTSULEc5MuLO1uUuymOyxZc0UY5ZEvPNw4OYrxQrXs/3/YFqbhnLg4Zf8ySOGS+SIqux1ihih/6hycwOFWu+1zRh2yOIUt+YTGzgNm7PF829HrS9ihWbbzpE49grFClkCVBXCpug+oYvYmrLEUfsgaCDPBGKs1UGif1mw4LCbKEpiCT0MTVocvCORsFqrJsOiyHCUdzsZCdgIW6ZFRlubRMly38yuefxX89N1j3Mfz3PlOxRO7YXMJ9/CLuUhoDVywHDx4diV0OeM6SI+W3Hh3K48DZSRy6UH/GJa+ynKtDxkPuC0mxKMc6ZMG+PDBZxrHhGQDN0YeMBBmxqnFZfdGlNnEfMkNr3IeMk01qXtsLOanfjoYsXSR0VVRuGrqKTFILjU6S/+fLC6a9ZE0jiflhOS4K1cUNLcknnoXmkXF3hhyypYGH/hqFFfm2n6kGF2ZRgfzQsRG86ROPhHNNxaighX933z4ygvd8dj/OR/K3ZNdNXhd+kdfI7Q1yyOa2Xvy9ZuZ4AZmvWHj73z2GI4N5UdgkI2/rKan6kjr1E8Ql0ihk2cxVlr2tKdx+VQ/27exs+LhcUvdCFTEHWo7luDA0FSlpsHrG0EOjk4DgipQfNBkDSktQgUXMH8txF92xDAmyBTtk8aEpYnHgTlMjt0iELCt23SKL93x2P44Nz4Q60gchy4X/xusVAJWl1wwLMm95voGb5cxbkHGHbG4hyxMXC3h+YBqHLkzFus7yb2FKEo7NcPFOgoxY1bguq2s1b8yq2NWdXeY1mhsJXcW//O9X4OpNrQ0fl/UFme26It8seiCzHOYJMv9+Q/MdMrPWIWPMC4115xIAKI+sGWCMeSHLRRZk8kl7oSflKjlkS0pVVCt6/z93fgrPnZ8KPYb/3uUcsujYIT8rISQ2FkOQ8WKOiu2AMYa/uO84To4WQq8ph0+5AGrkkInPMNeQpSRIOc6jAVoAACAASURBVO/69Pfwfx54KfbxF/NePt5MxRYOJEdRwjlk01I7DK0J1FATrAJBLJxGnfp/6cYUfvf7rlnmNVpcWlKeIDMdhmzSE1xxbS8MTRGCLKGryCZ0Ichc0fbCK4F3GdDT4o0SKVQXN5GcmD/85LrYVa+L4pCJKsvmF2SHB6ex8zfvwZHBPADg2HAe04tcKLHYiOR4fx/40/85ij/9n6Ohx/BtX6jYsW0vxqSCAFmACEF2CWKaO/MVy+vl9XcPn8A9zw/Vdcj4Ppxv4GaJHLI5JvVXIjlkrsvwvdMTeGEwPk9t1HcJi1WnJqk/l9RDIctpcsgIYvFwGMMCxqmtGrIJHcWqDdtxkfHbWdQNWfr5aIamIp2IccikxPHeVq/pLPUiWznyFQs7f/MefOHp8wC8UDJji5fTtxgO2WrqQ/bIi2MAgP98+hwA4Ec+9SQ+/eiplVylWRFFE0L4uOJ3y4nNIZO+j6dPB4PGZaHEq7FN213wfsVFU9VyxWtPFM3Q/iTvG0EOWf3jynz7kFV8ATXjXzyOFaswbbeuCzc64wnUQtVCKXKR05LUIyHLIIcMK6/HSJARqxvWJA39lopcSkfBr7LM+HMvo0n90ZClrirISlWWvA+Z4wZhMT68nHqRrRynRr1GsFw0uGz+LQouzlTwlHRClpGdgGbMIXNchj//1jGM5CuzP3gO8ErjsxMlOH4z0AkpabsZETlkUvf6aJgt5JCJPmSBmJGdolKMQwbUfv+24+Lrz10ICbWHj13EseF86HFcNFUsRwiy8Yggi88h88TS8HSlJhQf9CGbY9sLKxyyHJj0WmrUcz8vCkHm1IjblpQRFmRSyHKx284sBBJkxKqmUchyLZBL6iiYPGQ5i0OmByHLdEKPGZ3ExDIesqTWFysH3/aGlLwy39YXH/ry8/jRf3yypu/TV58ZCHU2X3AO2RKGLF+6OINP9p/E/YeHF+X1uAg4N1ESnzeuyq6ZiPbzsh0WcrmASB+ymJClLCrkNg/yY6Lf/+Mnx/H+LzyH5wcCMffrX34ef/NgOC8rCHs6QihOFKuhCwc5n60QqbL8gU8+XtPweP4OGW/74b32BV+Q1XP3A0Fm1zSGbUmFQ5ZyUj931lYSEmTEqsZx63fqXwvkkjoYA2bKVuCQRQ5kPIcsKYUsswkNZdMLgTlSDhk/4XOHrFE1FLG08JMXb1cCzF8g8z3h/37vnFhWNh382hcP4j++GyxbuEPGQ2SL3x5laMpzxiZLi+NMcMfk3HhJnGib4YIjX7HqOtHRPm9WzKij2RrD5iu2aGoqO0JmA4eMh/L49nFchvFiFWfGwu0t+PvIIcvxghkJjcaELCs2ZioWhqYrIsmeE52tOxtcTObn6JBxYVWs2jEOmY6q5cK0XTx0bATTJUvMFb5IgowgLo1GbS/WAtwVmyyZQpDJB1fHZXAZIlWWfmNYywk1bbQdJk4Mm9vTAJrDpl+v8G2flATZfEPIG3JeLuDnnzonTp5cdI9Kyd4LziHj8zCXwCEbmuaCbHHCirxiznYZnj03CQA1J+R6vDQys2QFAO///LP4ra8eir0v2vbCcljNd2VKDlFcH7J82cLGVu8Cq1yn+jH6msFYLV/smAyMee7iufGSKIyw5ZClGR+yLFuO2PY8iT5ftsT3W7bC+7Sc0yrjuAz/+J2T+NYLQ7HbiBcgDUyW/L/t2LAnb/1RiBVkXsjyM0+cxk9/Zj8eOzEmKvHJISOIS6RRp/61AM+LmSxZMDQVhqaEDsb8tqGpoi2G1/bCc9bkkI3tMnFQ7WlJQlXC4Q5ieeHbPhSynGeIjZ9Yx4umCOWIOYOXWGXJGAtyyJYgqX9o2lvfyeIiCTJJUL04UgAw9xDw//rrR/DWv3l0UdYjyki+WneUEA/HWVJeVVmINBdjharU9sISrpflMJH/NV220MMFmZxDFirqiHfdeEh6qhpU+r7u4w/jLf62MEXIMlivyaIZep9P9Z/EnZ94BIDskFm44H/mqCgKOvWH1+mDXzqIP/mfY/j4fcdDy4V4tFwMT1dwfjLYltELGMdlGCt4+1OhYqNYtcX4PE31LlSrthPaHnt6WwAAP3jTFqw0JMiIVU2jTv1rgWxCHomkwtDU0IE2EGRKqMoy67tpcvm57QYn2JShoTVtNH1bgLXG6EwVn/rOSTDGhDMkX1DMN8Qm58Oc952DaA6S97j5Cyrbd1+BxckhG56u4NGXRsXfgUO2OPvgVMnCFt/5nSh6bkdpDgKXOzADk+VFrXLlVO3a9gviPkl8AZ4wc1wGy3HxX88N4vV//rAI1VUsN1Q1yB2wfCVwyOon9UcdMu9v7pBxQRYlziGzXRYK750ZL2I4X0HFcsT+W6jaIrQY/Q6CWZbBezLGcO8hzxlrzyRitxEA3PwnD+KRF4N9KHr8miyZIipQNG2ULAddfs/FhKYiqauo2i56WpLiORtySbz0sTfjg3dcGbsNlhMSZMSqZq2HLHPSjEpdU5DQ1dDJkR+UoyHLtC/k5CtIx3VFLpChKWgnQbbs/MK/H8Cf/s8xnBwtCkEmJx7PN2RZsVxs8E8u5yfiHQmg/uikJ0+O11T1iefECP9L4V+fOI33fGa/OGEKh2yxQpZlC5v83MgJ33Wbi0M2Ljl0Z8dLDR65MEzHrUku58TlkAGeqB6eLqNoOqH1myjJF1jeY/NlGx3ZBBKaGs7tcuo7ZLLrBADTdQSZPKBcfu0LkkvFBeNkyRT7L2PAcb9is2TVulhA2CEbLVTFOkUvSuIqj/k+H22vwfPVurIJFCo2SlUbXVnvsQldRdLQULGcUCFCW9qAoak0OokgLhWX1e/UvxbIJQNBZnCHrE7IUu7Uzx0yuRLJdpg4SCd0FW1pI1RlRCw9x/1BxrqqYLIYzrsBFuaQbe/MwNAUUVUpn/y5eRznkD1zbhLv/Kfv4m8fiu94HpqLOAeHbbjo4vqP3IczY8XY+6eKXsht3HevFj2HrGxhk++QjRe42J3dIRuXcu0eOzG2KOsiU7XcuqHoqqiyDOdVVcygzYSc5ymHd/nFVb5ioTWtI2Wokcawc8ghs8IOWfRQasU4ZABEOFJev6mShWLVFjmRx4a8fb3GIYupsuQCryWl1zi8FdsR6RgA8JmfegV+/23XAqh1yLgQ396VQaFqo2g6aEnpSOresTOhqTBtN7Q/t2cMNAskyIhVzVpve8GT74FAaPETORAIMl1TxCxL3hgWCB/MbZeJA1FCUylkuQLwAcm26wYOWXXhDlnV9vrTbWlPi5ClfPLNJnUoSnxS/6N+I9V64Uwe1tJVpcYh+72vv4CP3XMktGyk5GKmauN0HUHGG3uOTFfBGAuqLIuLV2XZnUt4YtfftnPZnrIDtf+M19Pta88O4Ec+9cSirFcjh4znkHG3S3bIuDMk/4blvmqm46JiOTBtF60pA5mEHnqfRn3IzBiHrCNjYJMf+uRpIKLK0nbCDtlUWYg3/tqTJRPFqiOOWcf8i4/aHLLaSlEe3ryit6Xm8VXLRXeLF3ZsSerou7IHu3ty3raJTATg8y43t6VR9ZvHZhIaMgkNSV1FQlfhsnBYnwQZQSwSjsuwhlPI0JEx0OqHLQ1NwY3bO/D0mQkwxnBytIBvHBwE4OdHSCFLXp05Uw0LMktyyNozCaqyXCFMm4mk/rBDNs+kfstFUtewrTODAeGQBa+R1DWkdK2uQwYAndlEzX38tQEvbF6xXPze11/AiYveSfap0xN45lx45iJ/25k6Ioi7tcP5CvJlG2XLQVva8EaDXWLRgOW4KFRttKcTSCc04ZSYtjtruJW7admEJr6Lo0Mz2H92clFyyqp+933XrX2tStQhc7kjFYQIp+s4ZM+em8R3/HyqtrSBdEJD2ap1z+X34ZiS0AI8h6ynJYU3XtMLACKPLRSyjFYsSu494OVHmo4rwsZcDEfFaFyVJRdke3pyNd31K7Yjcmlv2d0FAGhN6zXbBgj2Md7WZ3SmikxSRyahI+ELMiDsRPOQZjNAgoxYtfCDpbqGFZmiKKIsW9dU3HJ5F8aLJo6PzOCzT5zBn3/Lq0iqGS4eE7J0nMCqNzQVbWkdU03eyXwtIZ/cLccVbod8cphvY9iK7SBpqNjWmZFClrIgU5E01JoTsu24eOasJ8jq5Zfxk3bOH3D/uSfP4v4jIwC8/So6mJ53j683sJ7nGo3kKxj088eu3dwKAJe8H/ILi/aMgWxCF4IMmD1syQsANralQrMiGYtvXlqs026hHqbkekXhotdyXLhu0DPQc8iCrve8UlD+XO/9twP4uX87AABoTRtIG1qoMWyj0Vn8fWWHrKc1iT+86zr89luuFusQ7dSfkCqCW1JhZ4mHMTe1pUPL61VZyn3ILkyV0J4xsKEliZLlhH4rFcvBLZd341ffeAX+/IduAOAJUMD73r/27AB+9nP7AQQXA1wUjhdNZBMa0gkNCT9kCQRi8Z9+ch9es7sbzQIJMmLVwi8413LIEgB2+oLM0FS8+nLvCvGJE+OhcEy0ypLPvQzlkEkOma4qaEsbyFcWd34iUR9ejg94J2AuQuQEYy7OTo4W8PcPnwBjrOH34zlkKrZ1ZDBZ8hqQyiffpO5NcKhGEqNfHCmIk1ecUOCvDYTzGCf8z5Av1zY75efdegPreThpJF8RA7Gv8FsOXGqlJXdK2tIGMgkNshlVL1zIGS+YSOgqOrMJIWK4exOtLnVdhms/ch9+/cvPz2m9bMcNVf1F4VWOtsNCAqVsOiH3rMN3MSfqtAhpTele78F6OWTRkKXjV1lKOWQ8UT7lX8xVLEdyyDxB1po2REVnNqmFXpO7XJf3eMerlpSOH923NZQqAdR3yLZ2pJFJeO16uFBkjKFiucgmNbz/jXvQ5ocX04YGXVUwXbaw/8wkHjp2EYwxsY/1+usIAJmEjmxCCzlkhaqNhK7if13T21RV+iTIiFULP9A10w9qKdja4V1xMsawtSODHV0ZPHV6IpQsGx2dVK/thekwJHSvoqgtbcBx2bzzloiFwXO8AC8Bup77AgBfe+YCPn7fcdxzaAi7PnwvDkvzCmWqtouUoWFzu3cCGpoqh07KCe6QRdoehJvGNs4ha5EqfSeKXluBmapdK8j8l6nnkBUkh4wLsMs3ZMXrXgq8OKUtY4j8Sc5sYeCxgonubAIJPSiY4aIhGko9dMH7Hr7+3IU5rZcs6OJacMgOmR0SUOFeWV2+IKtXANHqhyzrtr2o45Dx7zhfZaLJMM9FrVquyG3jIct0QsVvv9Vz0E5cLIRekyfmb+/M4KnfegOe+707cOVGzwGVw52iylISoAOTZWxpTwtnn4tovv1SRvg7VRQFrWkD+YqFiuWJ3pLpoFCxkUlowkEDgN09ObSmDWSTWihkKbt9zYI++0MIojlxWXxl0Fpja0cGADDoJ0Fv7UhjrODlRnBq217EhCz9K1V+IGpPewf5qZJVE34gFp/z0mzJuDEtKUNFwT9p84Hbn3jAq4B87vwUrt3cVvOcquUgqasix6ZkOjUhS5exGodMDhHW6+LP885kh2ysaAphVax67ipvF1D1BcXsOWRVkQu1qztXsz4LQXbI5N59wOwO2USxis5cAoaminXkIcmoIHv4+EUAwL4dnQ1f88DZCVy7uS203Rs6ZJJ7DfhVltL3yPP86s1/bE15IUu523zDpH4pN8xyXNgs+J75caRiOaKSk8+yTBsavu9lm3B8eAZbOtL4sDSBgHfQzyZ10aiWC6yiaQt3S55KAHgXmhcmy3jdng3iuFUyHew/PIy/vN9LyZCnWXDa0gamy7bIzZup2Jip2GhJ6SKHFgD2bmvHqy/vAmMMh/0JBNwhazaab40IYo5wQbbWQ5a82eWFKe+Al0l4OT2lOiFLXa0TsnS8gz4/ELX6V5FUabk8yK0Cxgq1gqwjkxDhvhH/xMpdCC6eo1RtL6k/LYWZZIGV0D2hHm0MygVRR8YIhSxH8hUhHLkYyUlifaJYFa6r12hYTiL3/o9zyCypA/3FfEU4Pbu4Q3apgqwUCLL5OmTjRRNd2aRoiQDUd8gePu4l0Tc6mY/OVPHDn3oSX9p/PuyQxeSyycPFZbFVtpyQq9mWNhoWL7Wmg5BlsWrj1j97CI+8NOa74fVzyOTqSb7duCArW44Io/Iig3RCh6Io+OCdV+L794Y72/OQpSzgM5LA4kSrLKfLFsqWg83tqdCFRf/xUTFxIRlxyAAvTDtdtkK5doWqjVxSD7m6V21sweUbctjd0yLlkDlihmUzQYKMWLXwPJG13IcMAK7f4jkj/ACYS+peF2rpIKfLDpmuQlM9gTYTClm6YhA5EE6MJZaOR14cxf8cGgqJYx6alPsreYLMW37Rd8g4UUEFePlMpuPlkHExXrackCOU1L1y/2hYcrJkQVG8XBv5vj/67yN4/xeeDb2nfHIbL5ghAS+HLUVSv7/sbX/7GD735Blvmf/ZVcWrspwsmmhN6ej2u6hPFOoLsqrt4OP3HatpcSDD3adcUq/JbZpLDlmXH7I0oyFLRxo95rh4fmAq9H5xDEyWwJgnpkMOWYxzWJVy1uQQXjnS9yuhqyGhE6U1FYQsh/MVDEyWcWhgCkk/laGRQ1Yxg+kdQLBPcvcM8JzYsuUgbQSSISpo+HvIDmVGCKxwLiv/zEDQj25TWzhkKfezS8UI4GxSR6lqh/q15Sue2y87ZLoUmhQ5ZBWrKR0yClkSq5b1ErLsyCZw+k/eIkJD2aSGYtUJCVHvyo/5t73lmYQe24eMz07k/XeoOezScvcjpzBWqOKWy4NqLi5assmgEWZnNiHCPiMRQRbnrsj5NSnpJBrNIVOU2hytqZLp964Ku2fTZUvkd/ETrNzeYLxohvapQsVGt597VOU5ZH4o8/DgNF427F1McDG6syuLU2PeqJ2ObAJJXUNXNoGhyOeV2X9mEn//8Elc0duCew8N4f97xTbcflVv6DFcVKZ0DWkjfFqbLUdyvFhFVy6B8aIpJfVzBykc9uP1FY1GMvHv7sx4KSTo4h2yOjlklht674SmoiVliEpVmaTvgqYNPTS+yGX+xRmrHVguRidZQYibCzEu7iuWUzM6iQtowMvdVRRArjlJGSou811PACKXNeyQhXuuDfvba2NbKujDZjo4My4JshiHLJPQMFWyxHlgpuLlNbakdCFeef6t2I5SUn+2gcBdKZpPIhLEHHHXSVI/gNBYj6zfhkA+yBm6gqQetL0AvANWTQ6ZFLJsk0KWH7vnCD7z+Okl/xzrkXzFQtV2QyEo/t3J7lNHNoGZio2q7dRUHcaNN+InWX5C5ssqliNOqkldxY3bO3B0KB/qSD9RstCRMZAytNBrV+2g3xR3d+QTl2m7GJwOxFPYIfP+n6nYqFguXBZ8Tu5u8arKw4N5dPgzCze1p+oO3wYg2nkcH57BfYdH8K0XhutvCyNo+cL380ZtL2b8pPCuXDhkycOH9aYVyA7Z/YeH8eP//F1RDTvsb59zE6WQ+xjrkEkhy/Coo1qHTN5XZHjqgReyDLcjMTQFKV2r7UMmdernFwSZmJClKQnTsuUIxwvwjkmGGpYQt1zeHRJPPAwqfxY70nONb6+NbUHIcrxoCudMXieZdMK7mOG91/IVS+SQtaUN/PEPXI8v//wtoeeIkGWlOZP6m2+NCGKOrJe2F1FyCR2m7YZCR4amYkNLEpqqBOXrRm3VVSip33fIvn1kBP/06Gn84yOnqAXGIvL4iTGcGy+JPBf5pMhPznIYqjNjYKZqi3l88oVGyXTwyIujobAdd1CShiqFmTzHg5f9J3QVd1zTC5cBDx67KJ47VTLRnkkgbWghoWjaQa6X3IdMRg4lFateaOkrBwZCIctoU1B+YXD1Jq/qbmCyjA5//9vclm4oyPh8yWf9RrRxkwCqlgNF8QRoxg9Z8srERuOo+Gvv6MyEQpbcxQkJMv8+TVVCv6unTk/g8RPjQsRxt+/8RCmUnzdryFLOITPDnfENLQhZygnu73zlNvyOX/WY9tt9yPl4XrGPN1D7+YEpfOXAQOh9q9L3nYoIspq2F6ZTI4yiYcvbrtwQ+psLuGJsyNJ77aHpChQF6GlJCgF3zJ+DyYlL6s8YngDlojbv98bj2+nHXrVdNIjliCpL02nKkGXzrRFBzBGeHLoODLIQ3LGQQxqGqqK3NYXHP3Q7bvUbHSY0NZS7wbtv8wNRJqHjbTdsxkP+iXpouiLGnRCXzvu/8Cw+2X8CeV+QVW1XuBz8BCXnO3X4fbB4e4xrfPECACMzFbz7X5/Cl/cPiGXcwUrqWsjVKJkOenxRntRVXLu5FZvbUvi239QV8NoncIdMdnFCgiymyhIATkuhpMdPjKHvL/rxgS8dRN70+21VbbHfceHCcxmv3tQSfF7fIdvcnhYVxHHwIoODfv7WqdFaQVaxvVw6RVGQ8UOWvDKxkUN2yhd3uzZkkdBUWNGkfqfWIevIGCFxxS+MuLAd8Z0d22Uh8Vhs4HJabniiQFTAJ3UVOX/fkR3LWy7vxl1+bikX5XKlZcLPLa1YDj792Gl81B93JTtklTohy6oVhFErNk/qD0sGwz+W8IvA1+6JCrJGSf1MbK8NuWSooTWvhuTEO2TeBaecQzZTaVwxLoswgxwyglg81kOn/jiiScuAF7IEPNufhzcTuho6EHql9Sx0IProXddhT08OP3HzdgAQ4oy4dKbLFsYKVeT9EF7VctDqnyx4e4tcMjh5tPuhp5O+4OBjYgCvvxhj4apZngeUCjlk3nzDXNJrhpnUNSiKgtddsQEHzk6iajt44cI0JosWOjIJJCMDqU3fRXWkNgzRXJvTkiD658eCMPdkJejUHzhkXJB5f+/uyQlXhTc73dyeQqFq103aj04gGC+aoqqS44VpvW3AT+q5pDcup1ECPnf7dnRmYUgOGf/scQ5ZeyaBqu0Kh4cLsmLVxoNHRzA0XRGf8aWR4AInrrhAdshkQVY0nXAOmZTUn5GqSOVjAV8uCzJDU9GaMjBZtDAo9agLhou7DUOWlnAMvaar6RqHzDuW/M5br8Z9///rRBPr6DqFQpauG/p/KF8RThZ//JEaQRbjkEUE2XTZQtF0GhY/yE4bhSwJYhFZryHLuGTUuKu9hK6GDuqOw5P6g+3VljFw/6++Dh/9/utx3ZZWPPrS6NKs9BrGdVmNQKja3tiZgckyHJeJRp/8ZMEdFu6Y8aRtADjpt7p4z6278I1feg02t6Uw4ocx5TBWRXLI+ImGO2TphIY/uOs6vOOV2wAAO7qymCia+OwTZ/C2v3sMF6bKaM8kalpiyM4Jv52NhADlZGtZ8E9WfUEm5TFFHbK2tCHauIiQpf93vbDl2fFaR+zUWLgpacVyRGNkHrJMJzTkknpNAv5UycQnHngRX3t2AKdGC9jclhKjdSyHhUYYxeWQ8fXmjhcvivnS/gG857P78b3TE9i7rR0ARNsGIL79BnfB+MUSZ7ocLsIwtCCHLK6KkX9eINzjLqGr2NqRxsBkCYNTFVRtb0STKUKWcUn9cshSHvfFkI70eAvSHxK4cmMLovBjFRfFrsvEcVt2yDaKvmW6+AzduaRYzvNjZTIJzReK3mvz/aderp23vsHrGBSyJIjFw2HrO2QpE02uBWrzLizX9ZP6a7teA8AVPS04P1E/l4eI5+5HT+GGP7w/VBnJT75cvDDmNUzlYadSNRyy1DVFnEhOjhZgaAo25JJ42dZ2pBMaLs54ry2HsbiQSuoqVFVBUle99gSmg7Sh44dfvlU0k+XVZg8evSiq4joyXjPRaMgSCLsjur9vbev0GhSXTEeIEiDIReTahbFgEkA5kkPWkjJEo+N2KWQJxAuy6ZKFfMUWYoFfTETzyCqWK1wU4fToml/YEhbL9x0exiceeAm/+p8H8Y2Dg8LV4eEsL3zI/G1cu234enPHi1edysL2ZVvbYWhKRLxGhmw7LmyXid+p/N1OFsPrHHLIJFdMFmf8trwfGpqCrZ0ZDOUrYnnZcqQqSymHjAsysT5eGFV2p2odMiV2OSfpV/lyh8yRclRN28VXDgzg+MiMmD2Z0FUxt3NXdwbbu7x9RW4JwuHijYtnvv+0zjFkuaodMkVRMku5IgQxX1yRQ7a+FFmcJa/FNDmMHnBEDlmdhoi9bSmM5CtiuxJz46nTEwCCpHMgcMBksTNdMgOHzD9BcXFtaEGO0MmLBfS0BKHnTEIXczDLpoNTowVcnKkIsSD6RyU0v+rMCYW1gECQPXNuUixrzyaQMtSQ62ZKbQf4mC3ubnTnEsKx6G1NiXYzV8U4I7xyrujPZBwtVJH0ZwnydeE5XpvbuCCrzSM7O+EJmpt2eI7T9VvaoKlKTR5ZOGSpi+2xqS0VqgoFgAtTXhL5ru4sXOb9DwS/F9MORgbFhSyFQ1YNQmVAuCHuhpYkWlIGxny3SlcVFKuOEA+A1FaEi3TZbYw0yk1oqghvhxyySA4iEJ4IYWgqtnWkwViQF1eSwqFVO8hV4w6brqkwNAUV22t7IYfV05HQIe/xVU+QKYqCbEIXn03+/BemyvjAlw4CgOjsL6/Hzq4s/vadN+KXb9+Nqze2Ikp0H+f7T66RQyYLMr35zhuzCjJFUW5RFOUIgGP+3zcoivLJJV8zgpiF9dKpP4p8QP6Dt1+L33nr1bEiLRk5eEb7kEXZ2JqC7TKMX+JcwfUGn8d4cjQIT8X1vpoqW2IosqiyTASCjF/ZD05X0NuaFM+TO8+XLAe/8O/P4E/vPRZyyACI9gYl067pVs9dKTkE1ZExkNK1UL5YNGSZ0FQhnPZua8ebr98IwKs05PvhVdLJku+HPGxWNh184EsH8bknzwoRwJ027qxtaElCV5XQJAMOLzLhPdy2d2bQ05IUvas4FdsV3dyDXCgV2zozIYECePl4PS1JvOvmHQAkQaZLgow7ZLFJ/WGHbMoPV8v9/JJ+mwouvDuyCdxzaAiv+uMHg3W2eB6ht83KpM+7ngAAIABJREFUli2eGxVkhpTUH8ohk44FPKTMO+bzz8S/e/l9+XdhOcEsW/l1U7rmi3IXranasKhYLy7IEvGCjN/Ht5Udc7HXktJx25U9NZ9pZ3cWva0pfOCOK2PzhKPvyfeJhiHLNeCQ/TWAOwGMAwBj7CCA1y3lShHEXBCd+tdZzFIWX1dtbMHPvPay2MdFDzi244aqLKPwVgnRpqREY/h2kwVZXIuDfNlCylCha4o4MfGTrKEpoe+1V3IM5BNl2XQwXjQxnK8EOWRGcFL0HCm3xrHoziVqQtjZhB4auQQEoqNkOmJfecXOTnzlF16N9/Xtxttv2AzAq4Lj6ys7ZLzajjtkJdPGgTOeK/fWl20CEAhY7rZpqoLObEKMc5J54sQYunMJ3HK5V+CwuT2NlKHVjDTycsgiIUtDw7aODIbzlVA4cWi6gk1tafzwvq2445pe9PliQAgyP5Qobw/5Ng9ZFqp2qCqVC7M/vOtavPOV20PCgDtDY4WqcKDFrFD/cWWTO2aG6EPHj21JTRUNeuWUBdkh6/Sbtsqix9BUbOsMN0ctmt568/2Br3dKSmVIJbTgokH6HG2REV7cbW8kyHjyPeDlsUb571++Fddsbg09HgiEcv3XjRdejZL65WPiqq2yZIydjyxqPByMIJaB9dKpP0q4smpuV4OaqvhOCKvvkPl5HMPTJMjmA0+LOSmF0eIcMpd5IoFvf1WBlBulhk58siCTXRDeiX2iaEoOmeb/r4oh3dFwjqIoIlR482XeYOxN7SnhKlUsF4yxIGTpO2Q8R+jlOzqhqopIVv+hm7aK/XBPb4v4DW7wu/Zzt8JlXv7PD960BX//YzcBAO64ZiO+9r5bcNmGXPAZk3pNWwjGGB4/OY5XX96N7Z0Z6KqCyzfkvFy5yCipalzI0tCwvTMDxoALkms0OF3G5vYUWlMG7v7JfdjdkxPfAQBYNosdLs4FVGfWc/ZKVSfUC5Bv+x9/1Q6kDA0tSXkGaCA2CyYPZ4cdMu4itab0QPz5lbcJySGTW0BkpNstSb2mL5ihKdjYmhJ5WQCQL3vvwxtDTxRNGGq4Wt0buxbuldeWNnD7VYGTJW+zeiFL7/MYQvTxULAsjKJzWuWQZSNCjp4UDeCNcuOQt89q7UN2XlGUWwAwRVEMRVE+CODoEq8XQczKes0hq3eFHEU+4CR1VQyDrncg4o5FNBxENIZfGPDqSKD+QOukroqTUcLPqQK8E4XsqPTUCVkW/Nl9E0VT9CFLSQ7ZhJ8MHudY8FDhb775ajz122/AVRtbpQTucEUdT+qPindFUfDiR9+Mv/iRl4XG03D3hjtksss6VjBDJ11VVXDj9o7Q62aTmih04Jy4WMDoTBWvubwLXbkkvv1rr8ddezf7gsz77P/59Dn8/L8diE/qNzTxmc/7gowxhqGpishbkwkcMifeIXPCDlnRtEPVkJMlCwlNFa6WLLD/zzv24opeT/jxilwu5nhImAs0ngsm3442hk34DV/lOY2KoojX4hia95hN7YHA58KRC5epkonoYSSla5jxB93ffFkXfum23Xj4g301xw55Kkg9tnakRdiYO4Xc1VWV2hBjELJsnLYu7+N8fNcVvTnsaiDkFEWRfnOrU5D9PIBfBLAFwAUAe/2/CWJFWa8hy6QeHPQbHQjlEu+ErkpJ/fE/++5cAqpCIcv5wmVMoWqLk2297vApQ4PuX6UnNFWcFAxNRVLXxHfT2xIfshwverlZkyVThMq4Q5bSNUz498c5Ftwh29WVRY//+nKLg9DoHtNBtU54O+E3YM2ldOiqgu5cUpzcN/sn/mjFZFsD18L7jHqNq/jUGa9Y4tV+uHJXdxa6v524GH3y5Dj6X7yIil3rkKUMFdt9QcZ7mU2XLZQtB5vaYwSZv+2rUg7Z4cFp3PHX38GkNOcyyCGrdchkp4YLDVUB3n7DZvz6nVf5j/Oewws1NvnikIf1LpNCdZ3+e8mjkwxNgaEpIedUPD7rCRN+jco/047OrKhG57luPDdsqmTVFPqkjGDsWnvGwAfvvLJG7AFB64i4Tvqc7V0ZnJ8swXGZELp8/2xLGzX5YemEht7WZEP3Hwj/Lvi2e1/f7ln7UialC6JmY9bpmoyxMQA/vgzrQhDzYr126vcqlzTkK/acQ5ZJXRWjk6JhDY7uj1+ikOX8cKVS/hOjBbx8R0fdgdZJKWSZ0LWaq/VcSsdE0QyFLGUnYNw/iVsOE7f5yTCd0ERTUC4aZN5+wxYkNA1tGblqLghZym5Q2XJg2fXFO+CFoja1p6CpCtrSBgYmy9ja4YUWrUiuUHumsSDLJjQhUKq2Fy49cbEgwo4ySUMVgne8aKJiuZip2CIHqjuXwM+97jK88epe9LQkkdBVDEyUkK9YeOSlMQDA5shIHSDYjpbDRGjtmXOTGCuYODVWqO1DVrVDgixfsUPFGLxIgwtYvg2mfFdtzG8Nwp1pLrB5CFXebmFBpsKQRkTJ8HBqdy6J0Zmq2K9++61X49DANH7jK8/XOGSTJRPR67q0oYnWJXpMSx2OoSpIG1po1m6UHZ1ZWA7D0HRZhPe5eI4LL/7gTVswVthQszxKRhoi/4E7rsCFyTLe5uc4NiKhq0C1OZP6ZxVkiqL8K4KLQAFj7KcX+qaKovwqgJ/xX/cQgJ8CsAnAFwB0ATgA4F2MMSr3IuqyXqssAS+vwxNkcw1ZajWjk+LY2JqikOU8kcd/Hhmcxu/81wvYKJ2YZeSQZVIKWXLXrEUIsuD58olHbo3Ahy+LKktDFa4xT/CWeeWuTrxyV2doWWiQdFSQzbKv/Mob9ohqQC4+skkdPS3JmlYTswqypI6zvov1V/e/iG8fHcH2zgx2dWdrTvZJXcVE0VtX/v6TkjulKAo+/JarxeO3dqRxdryEv7r/RXzmiTMAEOuQcfHitb3wNiTP/ZouWzD9vDURsqw6wu3iyM4kF1DcwWwXIULfIfPFc68vDnmvLlmQcWHtFX0YYj0NTW3okG1qS3mCzG/tcPWmVrE+/P35dzZZstASuUhLGqro31bvAo6vS6NjEICQS8ndQP5dxX0GPgpqNuQLles2t+HHX7VjTs9b7SHL/wZwj//vQQCtAAoNn9EARVG2APgVAPsYY9cB0AC8A8CfAfhrxthuAJMA3rPQ9yDWB/xEuB4FWTaph0JecYTGhOgqTMcLGTR6Tm9rikKW80Tu2/bNg0M4OpTHoy+NIeGfrOQr8VTIIQvEmXDI/DyhnjpVljJD02XoqiLyiORk7+5svCCMwk+Mlagg81seNNpXrt7UKtpRtKaDRProQGfv/tkcMl24XidHizg1WsSBs5O4bENtPlBS10QO2YTvqjEWP+8QAHZvyOHocB7PSj3Y4hyyuLYX/KudKlkipJtOaEgZ3pxY2SEDwusgpjD4r8udyYePX8Qb/rIfp8eKaEnpyCXD8x5DDpnveCUlhyxlaDBUJXa/4K0vuOsmf39cLPJ1bpMcspocMilk2UiUG7pad7tzdvjNXc+Nl8TQdv6cRhWRsyF//kZVnlH451mtIcuvyH8rivJ5AI8twvumFUWxAGQADAG4HcCP+fd/FsDvA/iHS3wfYg0jOvU33+9qyckm9VkPQslIyJIPEW50INrZnUX/i6NeD6omPGA1I/ykvaU9jafPenlPtsvQkdFFSJn32OJtLwAvZCJODv6JsyWlI2WoDXs/cYamK6HvWD4xxjlkcYRzyOSxTA4sm805rMNP7umE6rsgU2hLG+Lk3z5bDllSEyOOeJ7cTMUOVWJy5CrLCalfV7KOMNi3swP3HxnBBVXB7Vf14NrNraL4QEZO6pfnSgLcIQsqBLMJ3U/qbyTIAjEFBNvo/sMj/uxOGxtySRES5CFLed1EDpmmIZvU8TfvvBE37+rEF54+Fzuxg+d5cVEsf398XxQOmS+iGQMS0TwuIxC9jUKW77p5B26/qnF4cVObV+V5dqKEvdu9Kl0eXm7UxHU2ZDcyFTNaqR7BRVDzXcgvZGvsAdAz66PqwBi7oCjKXwA4B6AM4H54IcopxhhPvBiAV0RQg6Io7wXwXgDo7e1Ff3//QldlzhQKhWV5H2J+nJjyDmAvHDoEdbi28Hctf29WqQyNsYaf78zZ4GRRLRUw7c8aPHf6NPoxEPscPe/1KPr3ex7GZW2ND3K2y2C5QHqRO16vtu/t9BlPFLRrVVyQwpcas9FtuNBVrxoKAE6feBHlot/2oFzEoYPPAgDyU5Po7++HVaygzWD4zne+I17n7IX4odsXJopIahDbanyY5/wATz/xaMO8Hs5gwc+VOvgChnLB44+dOI2xSQcZQ5nTdzE95r33scMvwM77Y6FUG9P+/ccPPYvpU/VP7GPDJgpVGw8//DAujAUFAZXRc+jvHww9dny0ipmig/seeDg0CWHw/JmaxwKAOhnMi7wqOY2XJ4r4zneGah53Nu897tmDh0TbC87Boy8J4f34o9+B6lo4dW4QIwaQ1gHL9UZHVYt5sb3OD/nNUM2KWJbUgpYoozNVdOgWDh963lv/EW+O7BOPPSred+T8Ke/9nz2A8RMqWgEcmQRcs4LKTLXmuxnz95XSmLcdBgfOo79/BIBXYQoApwe9vyeGgm5WKrNDrzU5FszDPH70MLITx2u2F6cDQH//ibr3A0BXCjhw7Aw2mt4voTTj7RnFqfFL+q0nVMB0gWf2fxenknO7eDAr3v519vRJ9DvnFvzeS8Fccshm4OV6Kf7/wwA+tNA3VBSlA8BdAHYBmALwJQBvmuvzGWN3A7gbAPbt28f6+voWuipzpr+/H8vxPsT8yJ2ZAL77JPbecANed0XtVdpa/t6+PPgMnIsF9PXV79E8/NQ54OghAMCGzg5MXSwAqOLqK/eg75adsc/ZM1XGJ597CNqGy+s+hvNX9x/HPYeG8OAH+hb2Ieqw2r63Z6wXgRMv4aY923B4/KxY3t2Ww1d/5bUoVG3c8Af3AwD2Xn8tnp85i1PTE+hqb8XNr7geePJR9PZ0o69vH3qvzGO6bOHmy7rE61ReGMY/HToARQnnq5ku0NWSEtvq6eox3Hf2JDa0pHDbbbfNad0HJkvAYw/jsj1XYk9vDnjiCQBAz6atOFUex8b2FPr6XjHr6xxyXsJ9Z17Eza+4CYmzk7jv7FHs6O3E4KlxAMAdfbfGVulxjuIkvnnyGF596+tQ6n8AgCdavu91+/Cyre2hx/bnD+O58Qu49uWvAh54WCy/5oo96Lt1V81rv9p28OcH7odpu3jHHbeI+YhRTlycAZ54BHuuugbOs8+G7uvo3eJVwZ49jdtuuw1dBx9Brj2DtrSBtskxlEzP8drU0y22l/LiKD558Cl0tLagr++1AICuJx8M5dddvqUHr3j5LuDpJ5HKtQHjk3jDbX3A/fcCAH72bbdiKnEcP/ym60Pu2z/smUYuqYs5nJzyoSF87sgzePXea/DI8DHcsvcK9L1yu7g//dC3oCSzAPJ48y178Z/Hn4LLgExSD/3mHp5+AY9e8PblG294GfquWrAHAwC45vRTnkN2417gicfRs6ELGL+Iy7ZvRl/fyxb8urlHv42JoonbX//ahjMsZbqOPI6z+Slce9WVoW3TDMwlZFk7qOzSeCOA04yxUQBQFOWrAF4DoF1RFN13ybYiuKgkiFjWa9sLAPjQm64SMwbrkYjkkPFBz41CkZvbUtjQksRz56fw7lnW4exECSdHi+s+vMkYg6qg5uSYTerQIrk+KV0TidZyHzIeRrl6U/2ZfV3ZpKjM48jtJHgIp5HwiSKqLO35J/XL8BwxOYdMDr21zhKa4k1mJ4qeU8bFZ3SbAkHIciLS2b9eLlNS13DD1ja8OFKo6Vovw9vElGN+V9MlC+0ZRbRMSPkhvart+sUUXtVzXNsLeRu2ZRIhQdadS4ocwJLpQFcVKIqCX33jFfj8U+ewqS2Nv/rRvTXrc92WttjP0OX342pNG3j0Q7fBiIQbMwlNhCxbUjp2dGVxeqyIZCR81ynlIC5G8nvflT34yDcO48URbxQWDznHJfXPB77/NmpMG2VV5pApinJToycyxp5Z4HueA3CzP6y8DOANAPYDeBjAD8OrtHw3gK8v8PWJdcJ67dQPBE0+GxFte1Hyc1QaHWAVRcGN29pDCdD14MOUL85UaublrSdcxqAqCnb5jSy3tKdxYaocGhzOJyUkDVXk5CR0TeQX6Q3yWbgg62mpFWRvvm6TuJ1agCATVZZmpA+ZNXtSv8z2rgw0BejKJVAyw4KsJamHGpjGwfObeAPRn7l1F3Z0ZWNdD94YNjpzNWXUf4/feNNVGJ2pNgzjcqFcMmub+k6XLWSSQZsSLgqrttcXjeezyqKwVVRZyl3pw5+nO5cUXfQrliO29/vfuAfvf+Oeuutajxu3t+PX77wSt+7uFtWdMmlJkCV0Fbt7cjg9VqxpeyGL6cXItbrj2l585BuHce8hL1TMQ81xeXDzIZPQoKnKvERjQg+mYzQbjbbGXza4j8FLwp83jLHvKYryZQDPwPOln4UXgrwHwBcURfmov+zTC3l9Yv2wXjv1zxU5oTdpaCLcNduV4d7t7bj/yAgmi2aoa3gUXoU1kl/vgszbB1+5qwvveMU2bOvM4OP3HRfVcwCQ0lUUTSdcZRlpDFsPntTf05rEkUjq04/s2xq8h8GdtPkLspJZW2U5Wx8ymb4rNuCv+zLoaUmJHmQdGcPrUfb/2Dvv+DnqOv8/p2779prkm94JISEQIBgSmnDSVTzBjlL09Dy94nm20xM9T+/nWTj19ORUPPVUFAWRDkGkEyAkIT2k55tv799tM/P74zMzO1u/+y1JviGf5+ORx36zOzM7uzu785rXu43Q8gLw3yuvgevK2XX8xalTCi7rHcttOdXApar9zppdV/QxDzPgVOXSO5yivsLMCDJDoz+eIpG2CbkOGeS2vchO6odM+4+qsGhb01CZ2eZQMl1SmJeDoal85ML5RR+PGBoHEsPufmksbK7goVePoOX8hjYFBNlIYrocplZHWD69mse2iTw5bypBqUHg5RA1tVG5Y5D5nCejQ1Z0jxzHubDEvzGJscC2P+84zmLHcZY6jvMex3ESjuPsdhznbMdx5juO85eO4yRG3pLkZOZkDlmWQ65D5t8/wo++N6/w5QM9JZfrd5OTW3tP7q+q7TigiBL+f7t2Gae6g5KD4RhPLIR1DdN1YoJ9yEoJsjkNMdYubGTNApEnGTE03nnOTNYubGRaoJ+Wd2LywlbloKkKDRUmR/riviAL6aroS2bZfif2kVAUhaqQeF1TqsJcc/o0Vs9vIGpqI3bph4BD5o44aihRJeody14fNu/rX8ohK4egMMqlx62yzHLIUjaJlBjSHS4QOqss5JC5gmz1fNEupD6WcciGk9ZRd22C4fOQrvrzInsS2UUMwdFdE9VAdfmMTC6gPzVihAHiI+G1IBkN3udxQjaGBVAUZSmwBPCbtziOc8fR2imJpBz8thdSjxUkGLIIirORfvSXTa9BUeDlfT1cuKh4Mq/XOPJw73DRZU4KnOxj0Gt+GQzHeCfskKFm9yELzLIsRtTUueMDZ/PUrg53uxr/+pbT8pYbS8gSMiFWL2RZHTH8RrFjOWlpqsK3rl/h7rs2YlNYyOSQeSHLuhJ91HxB1hNHVxXqK0yO9CVG1fqgEMYIDlkyMHYspKtiXFNaJWrqeR3oQYgzTVWyc8jcmZ5XLpvGQ68eYX5Thf+8wymL2lG6PaMl2EKlNmb6znbHcHbv98Ysh2xifmCD4dpb1s7lPatmc96ChnFtM2rqI/ZBy2UyN4Ytp8ry88AFCEH2R+AyRB8yKcgkx5WTuVN/OXg/PKoiRpzk3l+MipDOwqZKXt5f2iHzyvdP9kayXg6Zh5fUHmx66Q1TDutaJodMU30hVs7JwXORiuXdREyxjdGELAFaaiNsbe33+05VRww/p2y8YZ3aqOnPzSyF95q8kGV9SYdMnIAP98WpjZlURwyO9CWK9iErF09sDRcTZFkOmZinmTRtagPvUVDwKIoYGB+8MDplaiVTqsJcsqSZTf/yF4QNzb+gSVmlmzZPBJ6D11IToSKks2JmDecvbOSC+v6s5RoqJjapH0RBg0dI1zhz1shh5JGY31SR1zNuJCZzyLIch+xtwHLgJcdx3q8oSjPwv0d3tySSkXGkICuJ5yRogW7uUN4P7Okzanjg1VYcxymYCO04jp9D1tp3socss4/BqpDGgzPvINLwfmARkDkRhg3VD1kW6tRfCm8bxeaXeg7RWByyR7e2ZQmynuGUKxDG99367rvOKKsbuxfe3dc1hKEpVJZYxxO3rb3D1EYNP1drvCFLVRVDuwdzBFllSKc/kaZvOB3IIROFBYmUyCFT/LBptihcu6CRFTMzobprTm/JGw1kZH03j+5vmXfsLGgWDXfDhsZPPnB2Xi+wo7FPwdD1RKWZfDowIqtcynGljxflHMFxx3FsIK0oShXQBsw4urslkYyMd2EkBVlhzIAgC+a2lHNluLSlip6hFEeKiK14yvaHux85yYeR246TVemrJAdY2HY/Mw7e69/nhyz17NFJiqJw1fJpnDNnZLfA+wwrCgyVBjhtejVvWdGSN69yJKbVRIinbP9zrI4YDCbSWLbjt4IYK3MbK7LGQBXDy21q709QFzNLVkN6Fxpt/QlqImbWSKHxYmqZ9jDePrW4+U7tA4mskKWosrQJ6ZrvzuUmmH/7HSt477mzSz5nsDXFRCTQl8L77i9sLr+b1UQ5ZMGQpX4c80wmc9uLonukKMp3FEU5D3hOUZQa4L8RHfVfBJ4+RvsnOYlIWbZffVMO9kk8OqkcvJOHpijMa4rl3V8KL4ckt9eTh5c/Bpz0w8jdnP4Mw2J8Ekde9e/y3Jtg2wvvRHfbO1ZwYRmNN8NuSLKYQ1YZNvjGdaf7w6/LpcUtDHitYxAQMxe9kUDGBE9hKEYwDFs/whxOLwTYM5SiMqz7rTEmQpAZusqgO8LJE2ReAnpbXzw7ZJm2SaQtQrrqi0QvbDwagjlaR1uotLsDzRc05Y+kKsaECbLoxDtkYyG3999kopSXvB34d2AaMAj8ArgEqHIc55VjsG+Sk4x//eMWXj3Uxy8/eG5Zy+e1vXAc2PMEHNkMisbMva/AumchVAl6CHDEMo4d+Ocls2YntWa1RM99zEdxm6C5z+/97V/dF3qcER5371NU0ExQdXGrhyBaB4oG6YS4v2oqVE7F76I52A5tr0KoChyHiq4OzlK2ckidk3VF7P/AOg507oR4X/a2ELk/IAYPF8KrsGyuCtHaFy8a2jwZcBwHNXiCGXZ7uLVtge698OrvuHi4j6c5nZCuZjWGLQvbBivpC7GiIcB0UhwDlVOzr1IGO2GoA/QwVDSBkd0cdXqFg4LNbleQNVSE/MT2vJNW/xEwYxCqyHxHRvu5Ow4k+tzj1IbkAKHEADXKAD1OjKUt+c1xgwSrFqsihi+cwsH3M94HyQHx3fe+Q+oIgs22aFb7SSeDoeGkn/g+mLSyHLJk2ibuVll6n39eYYGVEt/ZEleNQUFW8piwbRhohWg94ndCBU0X72dyEFJD0L0HKpqhqkU85pEWQsybqVrSIUsMQO8BZiuH2eNMKRzas21x4THQJvajZmbmPbbT0LNPfLbeb5dmUhMOOIHF3o9Ev9imEYFYU+Y12Dak4+Jfajhz61hQMQUqm7O34zgQ7xXfxXRC7JOdAivF3IFdnKZ0YGrnZ7bdfwg6d4nf2Cn5BTPHiqKCzHGcbwHfUhRlFnA98D9ABPiFoijDjuPsOEb7KDlJONg9zIHu8iv2stpeWCm45+Pwcia9cS7AaxO7j5OOUBUYURjqFD84ARqAX4eglxjRbR/jRm0vDUofNXuH4Jl1sOsR6M9pbKWoEKpiedUs/sOopHrDRpjyAXEiD+Dlj02vjXKkr5tk2iJkjK+n0IlKbg6ZL8hSg/CD82G4mw8Aa0MtKPc/yVW793Fj6EkO7bkCnlwM2+4TombgCPQeEAIiXCNOsKkhcfIBYpXT+D+zGr1tJjy6DKykEGHpYdj1qFjXsSFSB9PPgr6D0LtfnJiChKohWiu+M8lBlsR72BgK83TXafxUvZjayHyWKzupVfoxtVOgYwf86d9hz5PQ585ADVeL57YSQvRoIVYnhmDrHHF/chBqZggxooeEiIj3CIE60Cr2SdXFiRJxSfJyCPbbjQwm3wY72sSx2XvA/bcf+g5BcoizknFeCQ3zsj2fV5x/xNKbWaO+QmznMOx5TLwX/TkzLVVdiIba2eJEnxwQ+5DoE7fxPhju5gEcBjsi/FC/jPvN9wMwt8riDGU7Z6rbSdhvgvbtXLT3G5xtbGBzajZd2qcwsThX3cy0jkF4cKPY9yOvQsc2IZDq5wkxmx4Wn22sEZpOAVXHTA7yoPlH+oixKb4Wtg3A3j9D+zYY7oH+ViGCe/aJY0o1xPumqOJzSA2L7ea+3urp4vdhqEscC4rK75VKBkIqjS9cAXvnw6GXYLibU/uTcPj70LFdCBMc1oWgzakh+sBV4KRgsA0G2sXtYIcQQ6Ngnh7ht2YLL9iLiB7UYftOOPyKOIYOvSy213swsF1F7H86LpYpRd08MKOZi9XOXUXX+UvgL0OQ/tmPhJBrfUUcDwBn3gBXfWtUr2siURyn2NV/gYUVZQVCmC1zHOfo1ueWwcqVK50XXnjhqD/PiTZb70Tlhh89x8YDvaz/3CVlLX/PhkN89Bcv8fDfrWX+81+A538Iaz8B53wIHIc/PbuetRdcLL5s6YT4AfP/eW5U4Eot70pfKf6Y4+A7buKOzH3+44zweIn1bUv86FpJ98SbED+COOIEZ6WhZ6/44UkNQaxBXCk2LRYnQ1VnkDAf+fETfNS8lzMR4bO0o6Irtjgpz78I5l4g1uvZJ0SdY8FwN8m2HXTu2chUpUucAGaugku+CC1igMefd3Tw7tuf5fLFNZyz8xvb31nmAAAgAElEQVS8p+YV1JseESfhcXKifd8+c9dGHtjcygufdY/bTb+BOz+QWeCtP+R/n93D4oO/YaW+h7Rl8Ux6Aau1V1FwoPEUcWKN1ELDfCGU4r1CaJtRMGLuSWYH6ze8zHyzm+p0h7hPNQAH5r9RnOCjDXB4Axx4HqqmQcMCIUQqp4rjZKBNuGhDXcK9MCI4lVP41SPPcBHP0aj0YisaqntSHAo3E012in2Z/0aYvhKSQ+KkrJnCdUv0g5Xg4OFWWqKWcDfMGHS9Jr43qSHxesxKqJstBFHtLCFctZAQdGaMW+96gfPVDazRN6M4XuWcIva9ukW4PqFKOhMK975ymDdrT1KlDGXtL+FqmHcxTF0O4Sqxb7Ylbrv3QPdrwjEMVYrHQ1WZ21gD3366m6XpjVxkP80j4UsZHuzlSu1Z/6NMKwa6k8JSDDZb01mmvsbBymXgOLQMiLmxaCFxom9YCM1LhcgZ7BD3GVHxXvQdFELXsUE1eDIxhxhDnK7udrdhQuMiId4qp4rfsJpZQtj1HhC/Abb4rgo3qVHcVztHCPvuPeI7negXx1XtbHAskn1tDPW0UXP4SSFGq2dC1VQGOw8Sq6wRy01ZBvXz2La/lf6Nf+RMeyNKuAYqGsVnF2sQF2ixJnGflRavx/vNAvH5qrr43bISkE5i9eznhacf5XRlJyHFXa5yGuimeJ+MCFTPEK87OSheR7xXvC49AkY4/1ZRhXBtfUVcCOimuK2fJ963aJ04RlVNfFd0k3tf7eJPzz7HrQt3Y1pD0Hyq+O7Uz4fGxVBZuCHxRKEoynrHcVYWeqycthc6otXF9YgxR+uAL0zg/kk8HCfjWFRNO777chxIpm2Gc3PIrBRohfsYeTlkFbvuFWJs9cfgos9mHtfCYt1I7VHb58mMnrZYZw+wxVzFsx8/g7O+eB8JdB5/Vw21i9fmha6CqJbNuZ+5jy++weS9kSfglV/DT66GS74A8V6igw0sUob458OfYYr+Gs6gCk/dBpd/7di9wEmC7ZAdrh3qyvwda4Ql1zDUs5+vO2v4xc3n8N+PbeOrD+7iK2+cwTvOnCpOcGWG/XZM30dsZg3VjdHMRYVjFf2OlIMC3L7+cT57pId3hp/i3fPT3LY5hI7FPzRvJ7rgdHGRkxsWyt23detoGYeQvv3OJm63LmfPJ84QgqJqmviX89q6jvTzzy/+iW+n38ptS3dw7lRFXFiYMZiyPDtUN0r+uOFP/LjvEj6R/i7viD/IgBqm54wP86lnQ+y0p3LrlCdYdfpyfsvFfOK+w7xZ/TNfTvyKUKqXT6Vu5KarL2beiguFkB4FN33ufoZTFu+c2cO/XjZTCN8S38+xYrr/RMixG2JiiP3zBS6CFp0GXP7XE/bcGnDd4/dSTy+PvrOa6tlniFSJ8XLKVaNafE60j0fiC9HetnzSNbEsNcvyEuAdwOXAc4gZk7c4jjN4jPZtctGxU6jtaIEKpnivuMIaYw7Nj598jYbKEFce+T48+U1xFfVXT0HdnLHt675nxYFeM0GT7L0rj6OBbUOyH8xKUpYQZM7m36E8+U1x5bLh/2DJm+FNX8kLm9mOQ4gk9U9/WVxhXfz5o7OPJyjBpH6idVQ2ttDePghzLwSj9OepayrVEYPdTBPO2Dkfgl+8A+79ewDOAO4xNZJ2Ne9NfpLvr9hL5MWfCIeyojGzISslLjSO1vEzCXAcJyep3+3ftvBN4r3WTW5ZO49b1s4DwPDe+0hN9ntVBtefXeg7Pf7k5JaaCNuPDHCvfgkXnLmMuzc+D8AFb/gYVy8/NheHX7hqiRjVVTW15InaS+rvoJq9C9/PuQXfk7FhujNfP5W+mZdO+QR3berk+Te+iZc3P8Hh3jj3zPwkq84/DWX9AeAwv7PPY+kb3oeR6ucXj7Rxw6y1oxZjAKdOq+KFvd0ciiyAOWdP2Ospiqr6YuxY00k1zvxLYJTFJxPFkmlVBQe2TwZKXUp8Cvg58PeO44w8afh1TGPbk/Cdrwvb84r/gDPeIx5wHLj/U/Ds94SdfP3PREJg+3bYfp+wfxdfJXIadj4El3214Pa/cM+rNNPFlbHvwoK/gL1PihPfu39TWuTZIiGWcCAJdrgbfny5sLyv/KYIMSX6YdoK2Pe0SOJe8R6x3a7XhEXsXVE6Tv7zHXoJfnQFnHOLEDzlik4v+feZ74m8hzd+IX8ZKwXfXSX2afk7SKbfTYPTDfd8WuRFHHpJhB+2/gEOPAfv+g00LhTr7nqU1X/+ChtD6zH6LLjmdyMn7Z5kKIqCqaloblLuL25exQObW0vOpwxSFzPpGkzy6qE+Tpk6FeWWdSJR3Yyx6bdf5bU9u+hd9WX+9NARes64ksjmX8JjX4Ku3VA1HeashXWukH7/feNycSYzTqEcMrMC3vnLgssHZ1lOFrzWDqamUBXJnBaO5T7esLq8C9BQoN9YVRljmUaDoan+4Ourzl7I21dr1ERNmqrCHO6NZ41O8tcxw2CGgbZRz1X0OG9BAy/s7WYoMbq8rBMVOe6uMKWS+i86ljsyaenYwSlbvi4s5HQcnvg6rHi3yMP4w98KsbD0bbD3Kfj59XDpraR+80EMx61OW/AX0LlDnKRWfkDEx0H8ir/yS5gpKgpv1u8VMfjLvwY7HoI//kMmD+WSLxbOzXnym+LfxzeK3AmA3Y+LOH7dXPjdh0Q+g6LAKVfDK/8nlqlfIPbjO2fDkmtEvkX7NlGhmE7AFV+HxVcIZ+x3HxY5AH/+Bmz6LVx6q1inFAfWwx3XCFdkqFOIwzX/ICqzguxeJ8TY1NNhwy+YXnEOHzF+KvbhQ0+KHIGamXDwRfj52+H2S+D8fxTJvc98l2hkGv9jXcbb3no9DfMuHP1nexJg6qo/OLi5KjxiT6QgtVGDuzcc4u4Nh/iPty/n6uXT0JpOQVEUHpn1t3xjx3b+o3YKcIThqrk4p74ZZf2PcTQTxYjChp+LvKGevfDAp2HN3x/1/IzjgejUH7hjuLtkmNyYhJ3CW2qEq2Pqqt9oVfx/8p04g2JovMOpcwkKUNHJXnyO3rDtQoIspKssaK5gydSqrJFDo+G8+Q188+EdPL+3a+SFXwcUrbI8yZHvykg88kVs1YDrfgZn3SSSQg+/DL++AXY+DG/8F7j2h/COX4gkyTvfzxG7im8t/a1wlHY8IMQYwJa7M9vd9ke464NwxzXMVQ7xTu1REkuuFa7aWTfBOX8Fm38r/j2e46z1HxHbfP52ES599feZx3Y9IsKnNz0Ms1YLZwxFiLFl14skyN2PCbfMSsLGX8ODn4Xt94skyFgD/Oq90HcYXvyJaKPw9jvg8v8nnmvLPaXfr9aN8L9vEXZ4tEGIPysJrz0OB9fDr94n3C8Qzx2uhnfdCeEavjz4z1yqrWfgDZ8UTpgXcm05Q7yempnixP7Md2HJm7l/zW/4t/Q7Sc594zg+4Nc3pq6O+Wo02PG9rT/Bmq89xv89vx8QfciipuY7AinLYc+Sv6LXibL59M/BJ3bCBx6Av3kRTn8XPPcD+N4b8iv+gmz+HTz/Q1QrmalUPAHIyyEb7iopyLw2B5NKkHkOma76fb2AcTeGPRoERxEF93UiCPZDC/bf8gRZyGt7EXDCQobKmbPq+OPH1mSNThoNwcHbJwPSISvMyVmnXi4H1sOWuzkw63pmVzTC4iuFK/bbW0TlzFXfhjPfJ5addjr89QvYz/2AGx5u4ly9Gc77W2jfKsST4wjhdN7fi/YED3xaCIy+Q/zR/BQmabYsvJlTQThab/oKnH2zCPmt/xGc/8mMS/abG4WbBcJ92vBLOOO94jl2PipCReFqeN89Ylsb7xSu29W3Cbdu9zpRxaKZsOrDIsx62tvE9rp2w7fPgGf/SwimGefAosvFdjb9VrhTxWjfBne8Wbgi771bvD4rBV+bK9zAQy+Jyp/T3wWzV8OWP4jnrWiE63/Onjv+gc50iHlLbyKvS07tbPjQE6IEXA9DpIbks/sA2am/FKY2dkFWG8jxiBgah3vj7GwT5eEDiTQVId0/aSXTNgMVC3hj4vv8e8sZLNXcykyAa74Dy66DO64WxRdv+BtxLC29NhMuH+6G338EkgOsVk14sRr+buu4ErSPFaIPWeCOERwyczKGLGtEN33hkAVFyeT7bpn60QtZVgVee7A/WL0729Gric52yCagIa2m8j83rKS5jKkGrweOZ6f+yczk+UWYjDQthku+yP4ZboguWifERN9hEQJc8Z7s5SubGT7vU+x0pouBp4oCb/0BfOBBWH69cI/++wJ48HOiiuiqb8N7f0cH1dxlr2ZzKpDIqiiidPe8jwtX67EvZx7b+5S4rZoOqz8uetb0two3q++AKE/3tgFC9Lz1+yKEOPdCOPCCEGgtZ8Il/5IRYyBCnQsuFaHQvoNwwT9ltlM1LV+QOY4IbQ53izClosJ7fy/KnhVFPOf8i4QY7T0oSo93rxO9l1KDsOztYjuzV/NB/VZuTH2C4VJpFJVTRDI0slN/OQiHbGxvUNAh63Q79ve5Hdz742kqwzqGe2JKWqJJpoXmz0T0URSYez7MvwSe/o5wdu+6RYT7QZTxr/s3kQ+5+mP0VS0UKQFHNo5pv481ucPFT+iQpaYSNTVfxE+mffTQ3JmTMPEhy+D2gqKh1u0y3zMkjv/ckOVEcNHiZk6dVj0h25qseIJXlYKsIJPv2zaZMGOw+mNYeqBq5upvw6cPwHU/LagEvJNRMjiBXlVh5Y3wlu+LxojPfR8WXQHzLsSZeS7nJb7FP6Q+xK72gfx9qJ4O5/41bPiFEFID7aLM/ZJb4cNPC/EE4rEtf8BBwVl0WfHXtPhysX7HNj9/LY+LPiPy4t51J8wLpBJ6gizYu27L3cIB23KPaNnx1h+IXkpBLv2ScEn+6imYvRpn92O89tiPsCtbYOYb/MWS7ntX7vgkX5BJh6woQpCNbd1g8n/ngGiy2OeOTOpPpKkIG/6JMTj2KlHs81v7DyKn8KHPif+/9icRvv7eauHILr4SLvkiWxd/XDy+79nC25lk2Hmjk0YSZJNP7DRVhjA0xZ+v6QmTiRqbM9F4rtTEC7LgvMXMa/fc4p5hT5AFQpYT4JCdLNz7N2v41vWTs8JxMjA5v20nMN5JKWU5OI74BwhRtvx6uOHeTBsHIG07gIKDyu72Ih1F1vyd6HT9529A22Zx39RlorpyymmiAd/B9Viv/p7n7YXc/vJQ8R1sOVMIrelnZztjQaYuh7fdDgtyGrRWTRMJ/sE+S3ufEm0r1v8YUGBGgZLtmpmiEKJpMcy9EKXtVWZ0PsU6c22WqPUEWV4vsiLkjU6S5BEaj0MWCFl2+Q6ZaOjYH09RFdb9sFvKdcgg52IkyMxVMHuNyClUdRF2336/6OB+1bfhbT8CIBFuFO7v/hNDkDnkjO8a7i7cHsdlVn2MmKn5MyQnA6qqMLU6gumKCy83azKJxiAhXSVsqBMuhoIVpsGQZbXvkInvQbDSM/i3pDQz6qJcc3rL8d6NSYs8kiYYzyFLpW3+/tcb+LtfbcheYMpSePtPREiPjAgBaO0tMqQ5VClCpdvuE4UEIPpugahEbD4VNt+F1raZB6yV3L2hRJ4XCKF100NivdHgNasNjiU54grEg+tFiNWM5a8X5NQ30193Gn+2T+Mu7S+yHvJO5OU7ZOJWk4KsKKLKcmzrzqiL+tHqzgFXkLkO2UBc5JB5J2wx189zyIoIMhCFLi0rhevbsR2e+k8xKWDFu7N7lc04+4QRZLbjZLrBJPpFlXMJh2zRlEo2f/FNTJtEggzg5jVzuPYMcbL0nKfJlOcWJJRTDTpRFHPIVs2pZ82CBj5zxSn+8wf3RSKZCOSRNMFkHDKbPR2D/sDeYgTzbQbdgc0FOfMGEWp86jYwK3hkn8W/P7BVPNZyJnS/RlqPcZe1huoSia6O43C4t/x5kVlUuoIsmEfWtiXzdzlDWWtns/GK33FD6pPsSGYaEzqOQ8oSCite6oQewAtZKvIoLkpl2MiqHBsNq+bW8cQ/Xkh9zKRzMCdk6eWQBR2ytCvIcnPIgsw4C25+RCT0Axx8Qfyd20NuzlqRw/jkt+Gxr8COh3MGvk8enGAOmVcdegJOh3jPubN998JzyIxJKjZChpaVgD9RVBZJ6o+YGj+98RwWTxE9H2XIUnI0mJzfthMYT5AlLZuU5TCcLCGyyHbIBkstWz9P9HECmHM+D24+ws/dKkNazgRgx5z30EUVNSU6IG882Mu5X3mU2x4Zw2x4zyHrOwhtW+HFO2Cow52nR8a1GwHvhO0NqIbsMNdwcnQ5ZNIhK86t15zKrdeU97nkoigK02ujhA3NT+rvdZOaRZWlkamytBxfSCfSZXx+U5fBe+6CN/+X6C2Xy4r3iPzChz4Hj/8b/OxaeOrbY3odRxvb5nUhyIKcCA7ZRFdYQrZDZpQI9WeFLCepaJWceEz+mvITDD9kadmFZzPmLS8er4kaDI7UpfnifxbFAUaExN17fUeJU66C7j08aV8NG/dTHSn+sXon1q8/tJ23njl9dHksFc3Cjuo7DI/8i+ilBrDoMpHcP2VZWZvxkr573QRZyBam5eaQeRpO5pAVZ1b9CCHkMoiaGgd7hKvan0iTtmwGEsIh83PIgiHLUg5ZkGDBSC6aDn/5Y9HyZdl1cP8/wbqvimKT6smVg5IVshx28ysjxXPITgQ8sTNZBVlN1KAmMvGjd4IOmVYi1p8VspQ5ZJIJQh5JE4x3UkpbDknLHtHt8YRIXdRkMJnOFAEUo7oFonUk0nbGVQpXw0Wf5eCwsM5LdUH2EuEBnnutc6SXk42mC1HWewD2PZO5/8JPw6qPwJw1ZW3GO2EPBEK0QUE22ipLqceOLtFAs0vHgSP9Inwp2l6INz9p2b7QLplDNhoqm0Xblbo5YuxYOi4E2iTDIdAY9vXmkE1S9+cb153OF988yhzYMqjKyiEr/sNiaqr/uyNDlpKJYnJ+205gvLCN55ANjSDIPHFSGzNFgVaZYiSRtknnVLO19SWytlmIVGCdMZ04GxfD1nuFE7DsejjrZuI1C7i94mYsrbymhsHn9QSo7/YhBJllO7y8v6fkdrx1Zdfno0s4Zz7fwW7hllXmVlmmRxGyHC21s6FpiSgemWQ4wdFJrxNBVh8zMTRlUjaGBZhaHaGpcuKbqFZlNcUtfnpUFMV3yWTIUjJRyCOpBEf64nx33U7ahsoXLt7JKOk5ZCmrpOvliSevCedAqcT+nOexHbACjldbfzxrHwoRFD6lhNuhnmE++ouXGMrJa7tPvwgS7vibtZ+AK/4fT+zs5NY/vMqmgyXG4gSIB/av33292Q6Zzf2bWnnzd55kX2fxFh4yZHlsiOaMgznkhi8rQkamMexYQpajpWWFmPYwyZL7bef1l0P27lWz+OmN56BP0pDl0SKYQzbSdZ7njElBJpko5JFUgt7hFF+7fxu7e0Y+wdi2w/cf30WH2x7Ac8gcp/QJyhNPXs+nEfPIvPUCTpzHkTIcsrQdcMhKCLdndndyz4ZDbDncl3X/f7aewoBa6c6pnAdkQozF3MDW3jjr92ZmEwYdMs/VS1qZdYdTFnu7RHVqqYrQTGPYootIJoDc+XxePlm2Q+bkCbIRw++jZdoZQvB0vzax2x0nWcPFh7rBrMhu4XECUhM1WTW3fuQFX2eEA/lgyggXeiF3TuzJJlolRw95JJVgRq3o0N82PLIg29E2wFfu28q9r4iWECnL9sVSqbBlMhCyhBFaXwTInQjgOI7vkCVLhSzTAYesRMjS2w9P5HkMOwY/rf2IKDBQMl3aoXju1389vosP/jQTagou19bnuXrZSf1H3J5s3W4jxkJ4ydQj/XBKxkfEyC4S8QRZRW7bi5wqy+t+8Axff3DbxO1IyxnuDrw4cducALKGi4/QpV8yuRnNb0nIUKU7JplQ5NFUgoip0VQZon1o5Ct9L/fLd8jSdlmd5zMhS2GVlyvIvG2nvBYSiXTghGjz7Ud2cP+mw3nrpQIOWbyEQ9bvC7LsZrWW7fBE+MLMUHVG7rA/kEj7/au8/fNocxPEc3PIWt3n9apCC5E3Q1ByVMgNWXo5ZFVhHU1V0FQlrzGs4zhsPNBbfPrEWGhaIgbLH3pp4rY5AThZVZbd/qxVyeubkK5N2qIHyYmJbHsxArPqo7T1jpwb5VWYtfdnwoZpN7+rVC8y3yHzQpYj9C3zny+dGdEEmdCf2KbF/z6zl7Pm1PGmpVOz1ktbo3PIWnMEWdpyssKkYh9cQVbECUy64tSyHTRVyQqVdrgzEnOrLFvd19M1kC/IfvX8fhKWeH9lD7KjjxeyrArr9MXTfg6Zl29jaEpOUr9oizGcssouUikLzRDNhyeZQ+bk5pBJh+ykIKRLh0wyscijaQRm1EVpHx7ZIYvnuETB5PxSIcvcpP6yc8gC/c4gM2tQUcRjwykrq/Gqh7e8oSklc80G3HXbckKWlu2QtLLfj8QIDpkntrwCgXjK9quZBgok9Q8nLT+UWcgh+/lz+/jZM3vpGUxRE5345pCSbCJulWVzlahq80OWocwA6mRwuHja8kPd5Tb5LZtpZ8DhDWAfhUrOMZKVQzbcdcL3IJOUhxBksuWFZOKQgmwEZtXF6I47RfOjtrX2c+OPn89qcgo5OVFHMYcsV5BNqQqLhrRJi343TPjy/h5++MRud3khpmIhvWRS/4ArDL2QZdoSYai07eS12xhpBmUyx0FLpC2ipk7E0PzX6yX1m5rKYNLyQ5mFcsi6BpO09ydoH0jQWBkq+hokE4PnkFWGdSpCOkNJC1XJhDJNTSUV7EOWtv18xgl1yEDkkaUGoX0Cc9PGichllA7ZyUZI16RDJplQ5NE0ArPqozjAge7C7Rf+9pcv88jWNl7a113wcYChkjlk2VWWZbe9SGWHLD3hMqU6zGAiTdp2fJfrxh8/z5fu3UJ7f8IXUzFTL+2QJYSYO9IXJ2XZzP/Mffz7A9uwbDs/ZJn2QrMjOWTi8XjKJmyoVIT1gEMmtlEVMTjYPey38+gq4JB1DSbpGkpyqGdYCrJjgCe8oqZOk/t+V4R0X4QYmurmkLkhy5TtO6vlNvktm2luYv+hyRO2FG0vELFLKchOeK45fZofsShFdcQ4KuObJCcvModsBGbUiUrLfV1DzG+qzHvcy7EK9gPLJT4Kh2ykRrIeuQ6ZL8iqwrzmDjT3QpbVUYPOwSRP7Gj3l4+FtBFyyMR+tPUl/O38/Ll9WLaTlYAPGXdr5JBlxiEL6Rqqovjb9ly06ojOLjcRXFcVOnNyyOIpyxdxu9sHWdpSXfQ1SCYGL2QZMTXOmVvP7o5Bombmp8PUVdH2wu/Bl3HIJlyQ1c+HUJXII1vx7ond9hgRSf0qJAfATktBdoLzretXlLXc565a4hdVSSQTgXTIRmBGnZj1eKA7ux/W7X9+jStve8J3cLqHUnnrepSTQxY1hf1dTsgybWUKBjwh0z2YJGyoVEcMP3zqCRevfcfj29tJ2Q6GphA2tJIhS6/Ksj+R9gsVIoZWJKnfdciKnHwTXsgylfZfc9hQiYX0TMjSfR+CV5zzmyryHLLg/5OWLR2yY0DEd8g0Vs8XvamCxR6GpmTnkKUCOWQTLchUVQyxb3t1Yrc7DhxH7BZD7hzLqMwhOxloqYkwu2H8s2IlEg8pyEbACyXmCoPnX+ti08FM09SeEv2ySoUsk2kbVRFuUCykl1VlmQwIIq9qsmswRV3UxNRVv5H5QCKNZTu+MHtiRwfJtI2uiuqgUiHLwUTaL+Xf1T4ACEEmHLKcHLJ0fg5ZyrJ91zA/ZCkcsoqQnpfUH5wlt2RqFV1DyawGo7mfQ2OFFGRHm2hAkL1hXkPe44amusPFM1WWXg7ghCf1AzTMh44dE7/dMeK3X/G69Idl2wuJRDJ6pCAbAV1TiejQk+OA7c/JKSvVwLRUyDKRtjB1FUVRiIW0Eass/7S9nad2ZoaCB0OWtTEzL8l0IJH2k/u7BpP0DqcwNIWQrpUMJw3E00yrFu7grjYhyMKGRtq280KWfpVl4HW+9btP+U1Bk+nsTv6JtE3IzSHzQpbe6/DmUl6wqJGFUypJpm0GA9vNrbpskA7ZUcebZRkxdD+3Zl5jxhkw9ewqy7TtcNitxIxP1KDxIPXzYagDOndB25bsx5JD8L3V8OWp8PztE//cBfAbwybF94Rw1TF5XolE8vpC5pCVQYWh+A7Y+r1dGJqaF8LsHhxbyDKZtv3S6ZiZcYwGEmnueHoPH1w7zxcpjuPw3v95Lnv9oCCLmnll2AOJNH3DwRYcaQxNJWyodLszOh/c3EpzVZjlMzJX9oOJNEtbqjnYM8y+LiE+Q4aK7VC8D5lfaGCz+VCvn3uUW2UZT9nUx4RD5jmCnkN285q5nDmrllvWzuV3Lx0ERC8yr8VC12B2Gw7pkB19vHyxWEh8nq984VL0wLwq003qT6Rt33n1LliSlk3asssaL/OPd24gZTlc0zzCgvULxO3P/hJ6D8AH7oOWM8V9hzfAkU1gVsKT34QzbwD16LYm8IeLJ90muIYMY0kkktEjHbIyqDAVP0fs2u89zdX/+WRem4vSIcviYchE2va7PcdCut+r68v3buFr92/j0a1t/rK7O/K7nntJpd2DwiHL7RzdH0/RH0/5gmYgYaG7DlkibbOttZ8P/+xFbv1DJifHcRwGkmlaarPz54JjcoIEQ5LffHg7L+/vwXZgR1t/3uPiNVvCIQvpfiWoJ9rOmFXDRy6cj6Gp1FcIN6YzIMK8JH9DE4JA5pAdfbyQZaZBrJGV1G9oqn8hUe3mAB7pS/i9ueJlJj5vbe33j5mS1M8Xt127wErAr2/IDBxvfUXcXvQZ6NkHOx8u67nHgz9c3HPITCnIJBLJ6JGCrAyCDlmQt505nVOnifDEYAkXLDdkadsO9208zGAi7TpkGUHm9f/a7eZtBUOQz+zuJJeUlWkPURc18n0WXaIAACAASURBVEKWPUMpBpMWDRWZPmeG5uWQWXzh7s2kbYeX9vf4InMoaeE4MN0VZJ7bERwknb0P4oS7s22Abz68g9se3QmIQofOgYQf0vTEZiJlE9a1rLYX3jJmwElpqhSNSIPjm7oGk+iqwqx6cdKTDtnRx3M6o0Zhp8nQVT/0HGzUO61GHD+3PbKDT9+1ccTn6RtOMVROY+Ta2aC4+zLzXCG8+lvF/w9vgFgjnHUTROth029H3t44sR0HBUS4FKQgk0gkY0IKsjKoMApXUb5n1Szu+vDqvPvVnGk+uSHLHzyxm7/62Yvcuf5AlkNWEco0SvVGCtmBhPandxUSZCIk1BdPUxPNd8gO9wp3q94VLr4gM1TiKZvn93RxxswaLNvhqZ0dQKY6s6EihKmrHHYHfeuuK2XZDnagzYfngHnCKSgcd7YNZGZdFnDIUpZDIm2RsmxMTc0a7utVh+7vyoSHu1wnsKkyhKmpVEVk1P1o01wV5sJFjaycXbh60NQUP0+xOlAlO9sVzQ9vOcITO9pHfJ6+eLq80WG6KUQZCqz8gLive4+4PfwKTF3ujllaBu1bR97eOHH8HDLXwZaCTCKRjAEpyMogZigFk/Zn1EUxNMXP8fLwwoMewSrL3qEUX71fnCRSlpd343ZCDxl+8YA3pDyYeP/Svp68fUhZNj2us1UXy88hO9QjRJLvkCXT6KoIWfYOpUjbDhctbqIypPP4dnHS9ARZZVinJmL41ZLBQd7BIeVeuNHTaMExSDvbM4JsKDB8OqSrmTBqXDiFuWKyOmpQGdb9HDYQSf31MZMZtVGm10ayBJzk6GDqKj96/9lFe74ZmkpfPDtkCTCzXgjqI32JEastHccp3yEDmL4S5qzJNIrtfg3SCWjfIoQYQOMiUY1pH4XCggD+6CQZspRIJONACrIyqDBFA9O0ZfvJzFFTozZqoChKXiinMtC6IWJoWSHLDQd6stpSJK2MEJnTGKNjIEHvcMoPHwb7OBXq4p+yHLrdysNCVZbe3MEG3yGz/JClJ6RqYyanz6xhy2HRxsPL66oI6VkhqGDfsmDYMlkgR6guZhI1Nba39geGrLtJ/WmLsKEF8tqEIPPywoLMrItmVbR2DiSoi5l88rLF/Oj9Z+UtLzn2mLpasI/cLLep8kAi7X/26/d2sfJLD+WlAAynLNK2w2AyndXmpCjXfAfedSfUzAQU4ZDteEg0Zp12ulimcZEYs9R3cNyvsRT+cPHUEKg6aCN3eZdIJJJcpCArgwpDCIW2/oQvLhZNqfTdmbCZK8gyDll1xMgKWQYT1PvjaRIpyxdR8xsrADEf02M4mRE7ibSVVd0GwiHzwqm1USM/ZNlTKGSpEAqIyKqwQUNFiC73JOmFTWMhnZpI5uQS7OwfnGeZtPIF2fTaCLPrY+xwW2aAyCHzOv2HdNEYFjKCLHffQYQt97sO2ed/v4kX9/WwsLmSupjp55FJji9GIO8v6JDNch0yEILLcRw2HeyjYyDpN4718CqBbQfK6pShGaCHRPiyejq0boL7PglNS2DhZWKZhkXituPozr20HUc0hk0OigpL6dpKJJIxIAVZGXiC7JArbj7xF4v48Q1n+497Sc9ej6ZgyLImamS5XF6VYNTU6I+nSFqZpP75TUKQBfNtvHUdxyGRtv3KQ4+UZdPnumlV4UxSv6qInl6eQ9borpdIixYEQSetKmJQGzX91h1el/6KkJ7leMQDDllQhBVyyFpqIlRHjKxGrkNJy182bGi+cB2Ip0UOWSFBVhfhQPcwz73WxU+e3ss7z5nJP122OG85yfGjkCALGyqNblEGCKGVSNt+H7mBRIpP/HqDf3z2xTM5mvHR9pKtnQ3b7oW+A3DlN4VIA+GQwVEfRC6S+t0qSxmulEgkY0QKsjLwNJAvbipDVAdCeV5bAC9PK+iQVUWMrPyZzsEkhqYwrSbiOmQZQTajLoqpqzy2LdPqIh4YIu44UB/LripMWrYvlCKm5ouaqKlTEdIDOWSZ9UQOWUCQhXXqYgYDiTSJtOU7ZHkhy4B1EQxZBttgeOJ0Wk2EWEjLEmTDSct/PUGHbDCZJmHZWSd2jxl1URJpm8/fvZn6mMlnrzjFb1QqmRyYgVBzrTvZoqky7H8vPOIpyw+vP7Wzk1+vP8A/3rkBwL+oAEikywhZBqmdLW5nrIKZ52TujzVApO6oCzIH1xRLDklBJpFIxowUZGXgOWReP66YmZ207wmE5qowIV1lqlvuDyKMGMz98nKgqtwu9cEcMk1VmNsQyxrJ5Ik5z5HKc8jSjr9MxND8pH7PgfKeuz4gyExdzQ5ZRgx/uHnPUMpfR4QsCztk6SIO2fTaCF972zLee+4soqaeNeFgKGn57S280UkgQrfJtJ3V8sLDq7TccriPm9fOzep/JZkcBIX0PDfs3lwV8sW5x3DK8gW66obeOweS7GwbyGq0PGqHrG6OuD3rpvzHpi6Dfc+I5P5t95W3vZ0Pi/Bnmfg5ZMlBMKMjryCRSCQFkIKsDGKuIPMcsmgo+0QTDFk+9Lfnc/1ZM/zHmqvCWQnMnQNJ6mMhKsIG/fGUaAERqIz03LU3zKsnamp+yDLh3ub23UrbmZE1IUO0swDh2gVDp0Ehl++QGVkzOzsGkiiKEJPFHbLCblltzOTtK2cwqz5GLKRlhTaHUhmHLGyomZBlTj+2IHPdET3LZ9Rw85q5eY9Ljj9+Y2NTY1qNCFM2VYb9RrIeQ8mMIPNEf9dgkrd+90m+/MfMCKRRO2RL3gxnfxCWXJ3/2ClXiRyyn74FfvU+UYnZthW+MrO4c/bq3fDs98EuTxnawU79ZsXo9l0ikUhcpCArgwrTFWRFHDLvxBPWNWbWR7NCNY0VIQaTll+h2DGYpL7CpDKs05/Id4besmI6lWGdb1x3erYgSxd2yMQMQfFYxND8bUUMjRlulZuiZO9zbg5ZZTgzo7BrMEl7f5z6mImuqVRHM88XbMGRDJw0E2nbb/1RmxXKzX6fhpPpLIcsFmh7MZRMF8whm1Uf4w8fPY87P3RuXnsRyeTAc8hm1sd8t7ipKpQXWh4OCDKviritP0FfPE17fybJf9QOWf08uPxrIsk/l1OuAUWF3v2iq3/rJtHNP9ELW+4uvL3BDsDJDAsfAX+4eGoQDOmQSSSSsSEFWRmENTGq54DbfiE3N8YXZK475Z2gTE3NCgWCmMXYUBHyQ5bDSct3tQDeec5MXvn8pTRXhQkHWmZkBFl26DGVdnzRFjY0fx8ipsZN54lQjuOQJXZMTfVduZCuEja0LEHW1pfwE7KDIctEIDSZDvYhS1tUuW6Xtx0QjolHVVhnOJURpmFDJWpoKArc9uhOnt/T7Ye7clnaUl0wv0wyOfCOrZaacFb4vmDI0nWL+4aLz35NWKN0yEpR0QjzLhbd+wEOvgADR8TfOx8tvM5ge/btCNg24DtkModMIpGMDZmQUwaKotBQEWK/55DlNH71TjxeXpYvyHTVT3LuHkrSXBV2Q5YmqirGMaUsJy8M6bXTiBhBh0zcBpPzo6ZGyhIhS11VMDQVU9P8dc+ZW4+q4Pcd89A1xRduXs80Tzh2DyVp60/Q5M6IzO5DVjxkWRcz6R5K+a8XIJpVbWoylEz7bl5I11BVhRtXz2Ff1xBrFjRw3Vkz8957yeTHaxg8tTpCXczky29ZyiVLmv2myV5j4aFkJqk/dxZskPhoQ5Yjce0PwUrCf62BAy9ARZO4f/+zsOUPMPcCCAUuBnxB1lH2U2RyyGTIUiKRjA0pyMqkoSLkjxCK5TpkhheyzHHIdNUP4XUPphhOWgwlLeoqTCzL8XOvpgWKALK2m5VDJoRMTcRAVUTY0dRUUpbNcMoKiMJMDhnAhs9fmjWeCUBXMw6ZN3rIc8K6BpO09cdZPKXSvb9wk8vfvXSI+za28tkrl5C0bL89RjGHrCZq0DGQyMp3A/jslUsKbl9y4tDpjvnyjuN3nTPLfyysq/6c17a+uN/Hz+vsH8QTb+U0679nwyEGEmnecXYZIj5SI26nrxQOWcuZIozpWPDLd8Gb/g1W/VVmeU+QDZUnyLJzyGTIUiKRjA0ZByqThorCzg9kxI/nkJmBkGVN1AtZJv2msA2xUFZrjKk1YQoRNjSGkha3PbLD71bvdbgP6SqGpro5ZJb/3J4T5jWrrXSbvuqq4verNHXFF0RVrkOma6ovmjoGkjRVCYesuSqUN5sT4L5Nh/nl8/uxbAfLdvz+U1kOmZndIHc4ZWUqOGW15OuGQ+681GkFjuNgYr9XFAOFQ5bNrisbLyNk+asX9vO/z+wd3Y62nAFdu6FjO0w/C677XzArxX0eyaHMCKQyHTI/h0yGLCUSyTiQgqxMgqHC3NwYL2/GuzV0xb+t80OBKb8prEjqz4QCW4o5ZIbG9iP9fP2h7dyz4RAgnKXKsEFIFz3H0pZDPGUTMTOuHJA3zklRFF8oCofMFWSBHLG6qMmutkEs26HJzSFrqgrzwMfXcvlpU7K21zOUoj+R9gXW3IYYEUNjkeusAcQC1ah1MRPHyQwgDwpSyYnNBQtFCPDMWbV5jwUT+w8GWlsEQ5ZLW6oAcawBFJgQlkfackiPNtesyXVjD78CFc2iArN2FvTszywTdMXKFmSgO2mwU1KQSSSSMSPPimXS4F69Rwwtr9rPcwFCuSFL13UCkZvlOWT1FSE/rwZgSnVhhyxiaH4xgDceKaS77SLiYgRSyrJJpjMi0a+yzAmreuuKTv1KJmQZEEa1MZOtraIHmpdDBrCguTJvaLkXemp1w7gz6qJsufVNWcsEHTKvoa0X9pWC7PXDX66cztWnTyvYsDdiiJmv3UOpog7ZWbPr2NU2SE3UIGpqZSX1p207a8B9WXid+3Gg0r3AqJ4BPfsyywQT+ctM6ncch5DjvjZDCjKJRDI2pENWJp5DFgsVPulAxg3w5k2aukbY0IgYGt2DSZ7c2YmhKcyuj/oOWUOFmSd2/O0GRJWXDO01VA0ZKroqcsi8Yd2QaWlRSPCYeka0FXLIaqOmL/y8kKVHsZYTh91wVaEeYsH3qqFSOIXe+KncwgjJiYuiKEWnJ0RMjSnVwgEOCjJP0J87t56LFzezen4Dp7VUEzX1stpeJMfikNXMAt11oyua3ftmiJYYHkFXrFgO2VO3wabf+v+1HQh7gkw6ZBKJZIzIs2KZeDlkhTrF57a9UBQFQ1P88GFt1KB9IMGfd3Rw8eJmaqKmL5iKJfSL7QUEWcAhq4maJNI2qqqQtBwSKStr2dvfdxYLm/OrvTzRpGv5OWQAp0yt5OEtoiVAU2W2a2dohQWZ55AVaksRfK88QXuoZ5iIock2FicJZ8+uQ9dUth/p57A7xstzak1d5Re3rALgvAUNANy94RCJ9Mgxy7RlZ02LKAtVg4YFog9Z0CFL9MFwj0j+91yxqukw2Fl4O09/F6Yuh6VvBTyHzO2jJgWZRCIZI/KsWCZea4rcHmQQrLLMPCZaULjNUmMmj2xpo3MwydvOnA5kQnZTi4Qrg9sF/G7/pq7yT5ct4l/fchqmppBKi6T+oCA7b0GDn48TxBdkqupv26uyBLjR7VsGYl5nkOIOWdzfr1yCifuNviCLUyHDlScNn71yCf902WIihpjaUBnS/cKPcIFjplyHLG05JMfSr6zRHUxf4QqyGneqhueSeYKsaXHhkKVtw2AbJPozdzkQtsX3QAoyiUQyVqQgKxMvh6xQqC235QS4gsx3yEwGEmkaKkzOXyQaVHohy6nVxR0yL1EfMiGekK4yv6mS06aLZqlidJJNxBj5o/T2x9RFYcBXrz2Na8+Y7j9eEzX56rWn8cZTmvJCULpaePslHbJAyNKbMHCkPy7zx05C/Iax1WH/exIqEOYsN4csZdlZzYnLxssj83qRVbttM7zE/sEOkQdWM7NwyHK4C+w0JIOCzMGUIUuJRDJO5JmxTBpKOGSzG6KYmuoPwgbPIRMnHi+x/y0rWnzhUhXWWTylknPm1BV9ztxqTsg+iRma6PEU7ENWCtN3yITbVagR63VnzSx4v17EIfNaHozkkHkTBhwHKmX+2EmH971prgr5Y5LCBS4ioqZGtxuxdByHHW0DLGyuzFsuZdujzyEDOOVqEbJsWCj+n+uQ9bdCrAGiDTDUJeZZqoHvVn+ruA04ZI4DYVsm9UskkvEhHbIyqYkYaKpSsH/W/KZKtt76JmY3ZH6MTU3xxZfX+uLaMzNulK6p3P/xtVx22tSiz1koUTqYPG8UCVkWI5NDNvqPvdg6rSVClmFD9Xuf1QcaxsqQ5cmHd8HQXBUOjO3KP2Zjpu47ZPdvauXSb/yJ3e0Decul0k7WtIiyaVwIb78DDDekH2uESB08/R0xdPzIJmg6BaJ1gAPx3uz1BzxBltkn23EwbemQSSSS8SEFWZmoqsK0mrA/YqjQ40FChuaLpLesaOHvL1nI4ilVo3rO3NYVqpLtVBmBTv3lCDI/ZFkkQb8UxRwyX5AVEGyKIgSsqYuqTm+ZypCRt6zk9Y3XqHhKVdg/Dgs5ZBVhHbd+hef2dAGwt2sob7m0bY9NkOWiKPDOX4pmsPf8DbRvg2krMiOQkjlicKBN3OY5ZO4+hvLdPIlEIimH42JVKIpSA/wQWAo4wAeAbcAvgdnAHuDtjuN0H4/9K8bt7zsra7ZjKW69Zqnf6mHFzFpWzMxvmjkSuWHIkK75cy4hI8gSKbs8QaaNxyErLMj63S6ehRwyECEoJSnEWWVYp3MwKR2ykxCvUfGU6rDv1BZyyBorQ/QlHWzb4eX9PQAccUV/kJTlYDtg207exdComXE2LLsenvmO+P+0FZByBVYiR5B5Icv0MFhp0HRsxyFkD4r7Q3KWpUQiGRvHyyH7FnC/4ziLgeXAFuCfgEccx1kAPOL+f1KxsLkyrx1EMc5b0DBqRyyXXEGWK3oMTSWeskladlk5ZN4JsJjbVYpC6+S6dYWIhXR/v71k/gqZQ3bSETGDIcviDlljRQjLgY6BBJsPiSbFR/oSect57lg5zWFTlk0iPULp5sJLM39PWyFGKkEBh+xI5m83sd9xIGR5IUspyCQSydg45oJMUZRqYC1wO4DjOEnHcXqAa4CfuIv9BHjzsd63yUbYzHXIsj8uU1foj4v4TrAisxieMBpLD7BCrlqwh1qhkCWI5rBmThPaKumQnXR4FwxTRsgh89qt/GlHB8m0EFtH+vMdMi+hv5zE/n+5ZzM3/eSF0gvNfIMQU1XTRQWm53QFQpNAtiBz3TPbcQjbgyKhXx35wkgikUgKcTzOjHOAduBHiqIsB9YDHwOaHcc57C7TCjQfh32bVHgnMW/0TCjHUdBVUWUJhQsAchmXICvgkK2aW8c+N7/H1Au7blFTZ0AXYU3fIZOC7KTDc8imBNpeFHTIXEH26FYhfGqjRl7I0nEc3xkrJ4/sUE/c75dXFN2EN3w0I6iK5ZD1BwWZEGu242BaQzJcKZFIxsXxODPqwBnARx3HeVZRlG+RE550HMdRFKXgpa+iKLcAtwA0Nzezbt26o7y7MDAwcEyeJ5fWQRsFmBqx6B4CKxHP2o8jhzOhnL27drAusafk9jrbxPJbt2wm1rVtVPvy2p7M7EFThaQNs5VMn6YXX3ieA9H8E2x8IE4qYbNu3TrirtNxaO9u1q3bn7fsRHO8PjdJPl3tCVQFNq9/mq520eS4u7M97/M5PCAE1hNbWwlpMCNqs/NQR9Zylu3guL8Ojz/xJNWh0iH4I+3D9A87ZRwLq8AG1q0jPHyEVcDWDS/Q2lbtL3F2+2sYegwjPciLz/yJvuo2bAdS/e0MofHcSXy8ye/biYv87CYHx0OQHQAOOI7zrPv/OxGC7IiiKFMdxzmsKMpUoK3Qyo7j/AD4AcDKlSudCy644Kjv8Lp16zgWz1OIC9fE+d1LB3n1vq3UVldywQVr/MeeHt4Ce3cDsPy0U7lg+bSS23q8fzPs38OK5adxweLRGZD7n9kLWzcBEAsbJIdSXLT6LH6w+Rl6hlKsWX1uwSa36aYjdA0luWDlDP7YsYH1Rw5w5rKR93UiOJ6fmySbyMxOlu/u4qILF/BY7yaeOLiX2dOnccEFy7KW64un+NSfH6Q/BadOq2JJSzUPb2nL+hzjKQsevB+As1etKtlcGeC/tj9NjzU8umNhsBOehcVzp7P4nAvgwAsw9XR4sheal8DBFzjj1Pk4886H+/9IXRii4aaT+niT37cTF/nZTQ6OuSBzHKdVUZT9iqIschxnG3Ax8Kr7733Av7m3vz/W+zYZaa4K+00183LIAqHH0TSGHW/IMmJodJMiZurc+zdruPOFA0wpMKoJ4I1LMsLPm04gQ5YnH+fMreecufUAJassK0M6hgopG+Y0xGiqCtM5mCBl2f5xmwyEKcvJIUtZDpY9yiaywRyynn3ww4vh0i+J6sspS+HgC5AY8J06EbKULS8kEsnYOV5nxo8CP1MUxQR2A+9HFBj8SlGUG4G9wNuP075NOrwh3blVlsExTqOrshy7IFOUzH5ETY36ihAfe+OCsrbhDTKXSf0nN5nRSYV711WHFDqGHeY2xJhSFcZxoL0/4ReRBEVYOTlkYxqzpIdANUQOWZ+b2rr1XnE75TRxmxzAef6H/NT4MaaVhNDRd30lEsnrl+NyZnQc52VgZYGHLj7W+3IiEAsVrkpbOSvT26xQgnQuId8hG0PbC3cdXc1MIIgWmFpQikzbC9kY9mSmVJUlQLUpBNmcxpgv4lv74gFBlhFXqTIdsnKWy8OMiUrKoU7x//1ulsUUN8za34r26K2s0WAoWQvm6aN/DolEInGRnfpPACKu8MkNWS6fUeP/PZrGsGMLWYp1NFVB18RIpHJEYJC5jTEihkZzVWjUzy95/VCqDxngJ+nPaahg0ZRKVAUe2NzqP57MEmT5zlc8ZeE42S7aqEOWIEKQyYAgc2xAgaYl4v9P3eYvGk11y5ClRCIZF1KQnQDEvByyHNEVFFZlzbI0vE79Y28Mq6sqpiZGIgWnBpTD+QsbeemfL6EmWnj8lOTkoFQOGQQEWX2M6bVRrlo+jZ8+vZeuQVGdGQxZpnOE1kAizcovPczDWzI1QSlrjGOWzAqRQ+YJMoCqaSK/TA9DvCfnhcm2FxKJZOxIQXYCECmS1A9w03lzAKiKjBw+HJdDpmUcMkNT/UKD0aAoSlnCUfL6xruwKOaQrWjSuG7lDKrdMWW3rJ3LUNLi4S2iB1gwHyydI7S6BpIMJNLsD8y/TFtOnnArb0crsh0ygFrxfUMXRSzW7PMDy0uHTCKRjB0pyE4AYkVClgCfueIUnvjHC8sa6VQTNVGUTC7XaMg4ZAq6poxJkEkkEAhZFnHIljXqfPVtmXYYcxpiAHQOCIcsmc6IKy98ua9ziGv+888c6BFCLJG2s5YRvctGKcrMCjeHrCtzX+1sceu6Y6lFVwaWl4JMIpGMHSnITgCiblJ/oQHeiqIwoy5a1nYuWdLM3R85b8S+TYXwwpwZh0xWSkrGhp/UX2YOYsQQ47d6htyQpZ3f9mLToV42HOhl44FegKzZlV64ctQuWagCkoPCIauZJaouGxdlLZKadylJR8ssL5FIJGNEnlVPAKK+QzY+V0pTFU6bXj3ygkXWBRHuvPG8OQwnRxjWLJEUYSSHLBdFUdzxYUKQBfPBPHE25B6P7f1iGkXQIQvOvRxVxNwMJPXXzYHrfwZ187IWsStaaKeGFjplyFIikYwLKchOAKKGRmVIp6Hi+CXDG4EcsgsWNR23/ZCc+JTqQ1aM2qhJz5AY35XK6kMm/h5Oinmp7QOuIEtlhywBUrZNhFEospCb1K+ZUDMj038M4B3/B6lhbMeh3amhRenMzL+USCSSMSAF2QmAqirc/7drqY8dP0GmBXLIJJLxcOq0ai5c1Mip08p3a6sjhi/Isqos3b/zHbL8kKU12l5kppvUjwORuuzHFl0GgDOYpN1x289Ih0wikYwDKchOEFpqRp/3NZEYgT5kEsl4qIuZ/Oj9Z49qndqoya72ASA7ZOn9XSxkGRxEnhptt/5QBdhpiPdCtL7gIrbj0CYFmUQimQBkUr+kLDwhJgWZ5HhQGzPo9kOW+YJsOCUEWcdAtiBLjXLu5bce3sGHf7Ze/CdQNXkoVbhwJkuQyZClRCIZB1KQScrCG7c0lqayEsl4qYma9AwlcZzsnmLe30NuDpkn2hKuQBvtIPJXDvSw6WCf+E+garKHqoLLOw48Yy+ho/o0qJC5lRKJZOxIQSYpi4xDJg8ZybGnNmqQth0GEukc18t1yJLZ4UjPIcvu6j9yyLI/uP35l5AyRZ7bUHRKweVtx+FZ5xQeWv1zMZBcIpFIxog8u0rKwquylEn9kuOBN26rZyiVVWWZ9KosU+ms5b2k/uwWGSM7ZAPxgCCraOTRK5/kisSX6awpPDjcy0+TXwuJRDJepCCTlIXMIZMcT2oiYoySEGT5DtlQTl88zyFLpvPzzUohHLiMcEs4GpudOaSLrGq7imy0c10lEokkFynIJGXh5Y5Jh0xyPKh1W750DyWz5ldmcshyBFkqvzt/OTlkuSHRlBf6LBLuzDhk8nshkUjGh2x7ISkLXba9kBxHat1B491DyZzGsF4OWa5DNvaQpUP+rMxiYs5zyOTXQiKRjBcpyCRlIR0yyfEkO4csv3LSq7L0KBSyTI8QsoynLF+AOY6DoiiBOZiF1/U0njTIJBLJeJEhS0lZeEJM1+QhIzn21EQMKsM6d7100A9PKkrGAYunilRZFmiRUYyBREbUecsm/X5mhdd1fIdMKjKJRDI+5NlVUhZeyFI6ZJLjga6pfPXaZby8v4fvrdsFiBmvqWIOWWr0IcuBeGYb3nqeY2YVWTfjkMnvhUQiGR9SkEnKQpdVlpLjzOWnTWVmXZSkZaOpCoau+qHEYlWWqVGELIMOmSf0Umn3tsi6jswhk0gkE4QUZJKyUFUFVZEOmeT44iX3Cw3yBwAAEYNJREFUG5qCrqqkLAfLdkik7axjM5G2cRyHlB0sACjtkPUXcMgyOWSlHTIZspRIJONFCjJJ2eiqKjv1S44rXnK/oaoYmki69+ZYNlSITvmeLnt0axvbWvv8dUfq1J/tkGULsuIhS+mQSSSSiUFWWUrKRtcU6ZBJjiueQ6ZrCoamkrZsP3+sodKktS9OdUQMIv/rn7+UdbwWE1UeA4mU/7dXvVloSHkQT5CB/F5IJJLxIe0OSdloqoImh4tLjiO+Q6ap6JpCynaIu3MsG12HzGsiO5yy6C+QF1aMYFJ/MjdkWbTKUtzK6xSJRDJepCCTlM3chhiz6qLHezckJzG1riBTFBG2TFs2Q+4cSy9k6S2TS9qy6Y+nuHP9AT8ZP0h/iZBlsRwy2alfIpFMFDJkKSmb3//1ecd7FyQnObUxEbIcTFg0VCikLMevsGyo9ASZUXDdtO1wx9N7+fcHtrF4SiVLW6qzHg86ZJ4j5vUhK1ah6eeQyUtbiUQyTuTPiEQiOWHwQpYDiTSGptIzlOTFvd0AzGusQFXEbSHSls1jW9sA2HyoN+/xYFJ/JmQpBFfxKks5XFwikUwMUpBJJJIThppIxv0yNIUX9/XwpXu3ALCouZL1n72EVfPqC67bOZjkxX1CvG08mC/IstpepLMbwxZP6he3Uo5JJJLxIgWZRCI5YQjmh+k5ccKIqVEbMwnphX/WHtvWhu1ATdRg08H/3979B8tVl3cc/zx7zv64v/LzhhhJSIBQCpqiEAGpUwMzAoIDVLCDZSqttg6dOu2MnbGgM9WpZSy2DlU7nTajTOlUq9ZpqyNVpFNimFKkRcGIiMZUNCGUhCTcXHJz7967T/8437N7du9dCOzNPfvj/ZrZydmzZ+89O8/k3Gef53u+34l5rzfPQ9bcspyeremyT+zQO7c/pCPHZjLvYukkAIuDhAxAz1iRGR8Wt9zxO1yKJEnlOFrwvU8+c1SSdN3rTtUT+yfmjQubnK6qGH5mtdZcGZuYqmrPgRf0X3ue0/ade+rvYWJYAIuFhAxAz0intJAaq0Zc/UvrdOtbf1HrllckqW2FrDrnqhQLev1pKzQ9W9PuA5NNrx89PqvVI8mNAdWW+ceySzMdPtaYr6xWY2JYAIuDhAxAzxgpNapfh15IWocXn7Fat7z5zPrA+kqx+bIWFxoTGo+UYp0Wpm55+shU03ETx6taPZokfPWW5QKLl89k1sdkcXEAi4WEDEDPyCY+ew8nCdUZ4yNNx7S2LItRQVFIyIbLUX2+soNHZ5qOm5ia1apQgUuXWUqTr2yFbCbT6mRxcQCLhYQMQE96LlTINs1LyFoqZGGZJUkaLsZaE+YrOzA5XT+mVnMdPV6tJ2szL9KynJlNtp8+MlW/W5MKGYBOMTEsgJ7y7+9/syrFgt50x/2SpHXLKk2vpxWy0XKsyelZlaKC5kIla7gcqVKMNFaOdeBoIyF7YWZWNZdWjzS3LBsJ2fyW5Z33/Uj/9MheSVTIAHSOhAxAT9l8SjLx6+d/+yL9YP+ECi3ZUDmMIduwalhP7J9QMSrIQgtypJRc8sbHyjqYqZBNhCkvVocK2Ym0LNMKnUSFDEDnSMgA9KRLNo/rks3j8/aXQntyw8ohPbF/IkyPkewbCjcFjI+WmhOyqeTOybRC1q5ladZ4LX2PRIUMQOcYQwagrxQKpo+9fYve/abTJSUJWjpnWXqX5pqxsg5ONipc9YSs9S7L2ea5ykbLcSMhO55NyMjIAHSGChmAvvPOC0+rj/sqRgWlK1EOl0PLcrSs/9z9XP34eS3Luealk1Jj5VjT9QpZY1wZ+RiATlEhA9CXhoqRCiYVY2tMe1FMW5ZlPT9Vndd+XBlWAqi2LC6eGinH9SSNChmAxURCBqAvmZlGSrHiQqE+MWy2QiZJz72QjCNLk6vlQ0WVooJm5lxzteSRNVpJWpbVuVrTQH/yMQCdomUJoG+NVuJk2ouQWI1kBvVL0oGj0zp4dEY/O3QsOb4cqxiZZudq9SpZ088rx9p3eKppIXKJChmAzpGQAehbI+VYxdg0MxcqZJlB/ZL0dw/+VP/63X2qeZJsxVFBcVRQda42b/yYJI1VkpZl9g5LiYQMQOdoWQLoWxtWDmntWEXFKE3Iku+g56xbpvPWL9c/f2dffT3KZZXktWJoWVZnF66QzczWmsaPSbQsAXSOhAxA3/qrXz9ft//qFsWF5FI3Uk4qZJVipC//7iX6xDvO041v2CBJisP8ZaXIVJ2rzRvQL0mj5WKSkE21tixP5qcAMAhIyAD0rZFyrKFSVJ+HLK2QSUkl7PoL1ust566VpPo4sjgqaHauVr8Dc6jYWKx8tBxptuY6MtW8MDkz9QPoFAkZgL5Xv8uyFM177fWnrWx6XoxM1TmvjyFLq2pRwVQJ7z+YWQdTYgwZgM6RkAHoe2k7MlshS60KyyWllbJkDFnjLst0uaVSVKgvy5Sd5V+iZQmgc9xlCaDvpYP602pXqx/96VvrVbRSXGia9iJtWZbigspxmpBRIQOwuEjIAPS9qNC8uHirUtxoFsSF0LIMY8jSqlopLtSPa03IAKBTtCwB9L1iIV1c/KW/g6Yty3QM2XC2ZRkSsgOTM01tygI9SwAdIiED0PfSuyyzd0y202hZetN7SnFBpagxqD9diFxiDBmAzpGQAeh7cVRIFhs/gcyptWU5tECF7ODktNY0JWRkZAA6wxgyAH3vNa9epn2Hp07o2GJYOqna2rLMjCGbnq0lyy/tT95DPgagUyRkAPreTRdt1E0XbTyhY4txkpAdr85JahnUHzWaCuOZCpmJjAxAZ2hZAkBGMbQsX5hOlkdaOZzMU5ZtWUrSqpFifZsxZAA6RUIGABlpy3JyOqmQrRhOEq/sPGSStKySTcjIyAB0hoQMADKSlqVrcrqqqGAaLc+fh0ySlg2RkAFYPCRkAJCRtCxrmjw+q9FyXJ8yo3UM2bKhxhBc40oKoENcRgAgo1yMdLw6p8npOY2WYxVDElZuGUNGyxLAYiIhA4CM5UNFTc/WdHByWqPlWFFmjct2LUvSMQCdyi0hM7PIzL5rZl8Lz083s2+b2W4z+6KZlfI6NwCDKx3Ev+/IlEYrcX1h8nkJGRUyAIsozwrZH0h6IvP8Dkl3uvtmSYclvSeXswIw0FYMJd8F9x4+ppFyrDgsTF6KXmQMGfkYgA7lkpCZ2XpJV0v6THhuki6T9OVwyN2Srsvj3AAMtrRCdrxa01g5VhxalsXWQf1UyAAsorxm6v9LSR+QNBaer5Z0xN1nw/O9kk5d6I1m9l5J75WktWvXaseOHSf3TCVNTk4uye/B4iJuvSnvuD01MVffPnr4gHZ977Ak6emf/0w7dz6j2KSapIcffKB+3AM7v1Ufazao8o4bXjli1x2WPCEzs7dJetbdHzGzbS/3/e6+XdJ2Sdq6datv2/ayf8TLtmPHDi3F78HiIm69Ke+47T18TB9+8H5J0uaNG/SG89ZJDz+oX9h8hrZt26zK/feqGJkuvfRS6d57JEnbtm0b+IQs77jhlSN23SGPCtkvS7rGzK6SVJG0TNInJa0wszhUydZL2pfDuQEYcCuGG/cTjVYaLct0lv5SXNBYpfnSOeC5GIBFsORjyNz9Nndf7+6bJN0o6T/c/SZJ90u6IRx2s6SvLPW5AcBIKaonYWPZQf1xY3B/dvyYJBljyAB0qJvmIfsjSe83s91KxpR9NufzATCAzKxeJRspZ6a9iBqJWfYOSwBYDLleVdx9h6QdYXuPpAvzPB8AkJI7LQ9OTmu0Ems4rGU5Fqpiq0dLWrd8KM/TA9CH+JoHAC1WhFn4x8qxTl0xpM//zkXaunGVJOlvf+MClaNIknT1lnW6Z9f+3M4TQP8gIQOAFulcZCOhOnbJmeP1104Zq9S3P3nj6/Sx67cs7ckB6EskZADQYnmYrX+0/OKXyDgqaFnUTUNxAfQqriQA0CKtkLVObwEAJwsJGQC0SMeQjbxEhQwAFgtXGwBoceVrX6Wj07NaOVx86YMBYBGQkAFAi7PWjumDV52T92kAGCC0LAEAAHJGQgYAAJAzEjIAAICckZABAADkjIQMAAAgZyRkAAAAOSMhAwAAyBkJGQAAQM5IyAAAAHJGQgYAAJAzEjIAAICckZABAADkjIQMAAAgZ+bueZ/DK2ZmByQ9tQS/alzSwSX4PVhcxK03EbfeRNx6F7FbOhvdfc1CL/R0QrZUzOx/3H1r3ueBl4e49Sbi1puIW+8idt2BliUAAEDOSMgAAAByRkJ2YrbnfQJ4RYhbbyJuvYm49S5i1wUYQwYAAJAzKmQAAAA5G8iEzMzuMrNnzez7mX2rzOw+M/tx+Hdl2G9m9ikz221m3zOz8zPvuTkc/2MzuzmPzzJI2sTtHWb2uJnVzGxry/G3hbg9aWZXZPZfGfbtNrNbl/IzDKo2sftzM/th+H/1L2a2IvMasesCbeL20RCzR83sm2b26rCfa2WXWChumdf+0MzczMbDc+LWLdx94B6SfkXS+ZK+n9n3cUm3hu1bJd0Rtq+S9HVJJuliSd8O+1dJ2hP+XRm2V+b92fr50SZu50g6W9IOSVsz+8+V9JiksqTTJf1EUhQeP5F0hqRSOObcvD9bvz/axO5ySXHYviPzf47YdcmjTdyWZbZ/X9LfhG2ulV3yWChuYf8GSfcqmb9znLh112MgK2TuvlPSoZbd10q6O2zfLem6zP6/98RDklaY2TpJV0i6z90PufthSfdJuvLkn/3gWihu7v6Euz+5wOHXSvqCu0+7+/9K2i3pwvDY7e573H1G0hfCsTiJ2sTum+4+G54+JGl92CZ2XaJN3CYyT0ckpQORuVZ2iTZ/4yTpTkkfUCNmEnHrGnHeJ9BF1rr7/rD9jKS1YftUST/PHLc37Gu3H93hVCV/5FPZ+LTG7aKlOim09W5JXwzbxK7Lmdntkt4l6XlJl4bdXCu7mJldK2mfuz9mZtmXiFuXGMgK2Utxd1fzNwgAJ4mZfUjSrKTP5X0uODHu/iF336AkZu/L+3zw4sxsWNIHJf1x3ueC9kjIGv4vlGkV/n027N+npO+eWh/2tduP7kDceoCZ/aakt0m6KXwRkohdL/mcpOvDNnHrXmcqGY/5mJn9VEkMvmNmrxJx6xokZA1flZTeRXKzpK9k9r8r3IlysaTnQ2vzXkmXm9nKcEfm5WEfusNXJd1oZmUzO13SWZIelvTfks4ys9PNrCTpxnAslpiZXalkPMs17n4s8xKx62Jmdlbm6bWSfhi2uVZ2KXff5e6nuPsmd9+kpP14vrs/I+LWNQZyDJmZ/aOkbZLGzWyvpA9L+jNJXzKz9yi5A+XXwuH/puQulN2Sjkn6LUly90Nm9lElfyQk6U/cfaFBlFgkbeJ2SNKnJa2RdI+ZPeruV7j742b2JUk/UNIO+z13nws/531KLiyRpLvc/fGl/zSDpU3sblNyJ+V9YUzLQ+5+C7HrHm3idpWZnS2ppuRaeUs4nGtll1gobu7+2TaHE7cuwUz9AAAAOaNlCQAAkDMSMgAAgJyRkAEAAOSMhAwAACBnJGQAAAA5IyED0PfMbLWZPRoez5jZvrA9aWZ/nff5AQDTXgAYKGb2EUmT7v4XeZ8LAKSokAEYWGa2zcy+FrY/YmZ3m9kDZvaUmb3dzD5uZrvM7BtmVgzHXWBm3zKzR8zs3nTJNQDoBAkZADScKekySddI+gdJ97v7FklTkq4OSdmnJd3g7hdIukvS7XmdLID+MZBLJwFAG19396qZ7VKyPNM3wv5dkjZJOlvSa9VY7imStD+H8wTQZ0jIAKBhWpLcvWZmVW8Msq0puV6apMfd/Y15nSCA/kTLEgBO3JOS1pjZGyXJzIpm9pqczwlAHyAhA4AT5O4zkm6QdIeZPSbpUUmX5HtWAPoB014AAADkjAoZAABAzkjIAAAAckZCBgAAkDMSMgAAgJyRkAEAAOSMhAwAACBnJGQAAAA5IyEDAADI2f8DHV8Wtfi4QNEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "forecast=[]\n",
    "for time in range(len(series) - window_size):\n",
    "  forecast.append(model.predict(series[time:time + window_size][np.newaxis]))\n",
    "\n",
    "forecast = forecast[split_time-window_size:]\n",
    "results = np.array(forecast)[:, 0, 0]\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "plot_series(time_valid, x_valid)\n",
    "plot_series(time_valid, results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hR2BO0Dai_ZT"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.65494"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 结果对比，计算误差loss和平均绝对误差MAE\n",
    "tf.keras.metrics.mean_absolute_error(x_valid, results).numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vXaFHOaJqE6M"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 从训练集和测试集上获取结果列表\n",
    "import matplotlib.image  as mpimg\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#-----------------------------------------------------------\n",
    "# Retrieve a list of list results on training and test data\n",
    "# sets for each training epoch\n",
    "#-----------------------------------------------------------\n",
    "mae=history.history['mae']\n",
    "loss=history.history['loss']\n",
    "\n",
    "epochs=range(len(loss)) # Get number of epochs\n",
    "\n",
    "#------------------------------------------------\n",
    "# Plot MAE and Loss\n",
    "#------------------------------------------------\n",
    "plt.plot(ehs, mae, 'r')\n",
    "plt.plot(epochs, loss, 'b')\n",
    "plt.title('MAE and Loss')\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.legend([\"MAE\", \"Loss\"])\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "epochs_zoom = epochs[20:]\n",
    "mae_zoom = mae[20:]\n",
    "loss_zoom = loss[20:]\n",
    "\n",
    "#------------------------------------------------\n",
    "# Plot Zoomed MAE and Loss\n",
    "#------------------------------------------------\n",
    "plt.plot(epochs_zoom, mae_zoom, 'r')\n",
    "plt.plot(epochs_zoom, loss_zoom, 'b')\n",
    "plt.title('MAE and Loss')\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.legend([\"MAE\", \"Loss\"])\n",
    "\n",
    "plt.figure()\n"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "include_colab_link": true,
   "name": "S+P Week 3 Lesson 2 - RNN.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
